Overview
Dataset statistics
| Number of variables | 46 |
|---|---|
| Number of observations | 12192 |
| Missing cells | 205935 |
| Missing cells (%) | 36.7% |
| Duplicate rows | 0 |
| Duplicate rows (%) | 0.0% |
| Total size in memory | 101.5 MiB |
| Average record size in memory | 8.5 KiB |
Variable types
| Text | 36 |
|---|---|
| Numeric | 4 |
| URL | 1 |
| Categorical | 5 |
Source has constant value "Scopus" | Constant |
Conference code is highly overall correlated with PubMed ID and 2 other fields | High correlation |
PubMed ID is highly overall correlated with Conference code and 1 other fields | High correlation |
Publication Stage is highly overall correlated with Conference code | High correlation |
Year is highly overall correlated with Conference code and 1 other fields | High correlation |
Language of Original Document is highly imbalanced (92.6%) | Imbalance |
Publication Stage is highly imbalanced (92.2%) | Imbalance |
Authors has 384 (3.1%) missing values | Missing |
Author full names has 384 (3.1%) missing values | Missing |
Author(s) ID has 384 (3.1%) missing values | Missing |
Source title has 3321 (27.2%) missing values | Missing |
Volume has 4208 (34.5%) missing values | Missing |
Issue has 7629 (62.6%) missing values | Missing |
Art. No. has 9589 (78.6%) missing values | Missing |
Page start has 3148 (25.8%) missing values | Missing |
Page end has 3305 (27.1%) missing values | Missing |
DOI has 2068 (17.0%) missing values | Missing |
Affiliations has 558 (4.6%) missing values | Missing |
Authors with affiliations has 384 (3.1%) missing values | Missing |
Author Keywords has 2918 (23.9%) missing values | Missing |
Index Keywords has 4826 (39.6%) missing values | Missing |
Molecular Sequence Numbers has 12188 (> 99.9%) missing values | Missing |
Chemicals/CAS has 12059 (98.9%) missing values | Missing |
Tradenames has 12174 (99.9%) missing values | Missing |
Manufacturers has 12183 (99.9%) missing values | Missing |
Funding Details has 8305 (68.1%) missing values | Missing |
Funding Texts has 8043 (66.0%) missing values | Missing |
References has 740 (6.1%) missing values | Missing |
Correspondence Address has 5302 (43.5%) missing values | Missing |
Editors has 9435 (77.4%) missing values | Missing |
Publisher has 1121 (9.2%) missing values | Missing |
Sponsors has 9667 (79.3%) missing values | Missing |
Conference name has 6503 (53.3%) missing values | Missing |
Conference date has 7267 (59.6%) missing values | Missing |
Conference location has 7265 (59.6%) missing values | Missing |
Conference code has 7267 (59.6%) missing values | Missing |
ISSN has 4939 (40.5%) missing values | Missing |
ISBN has 5604 (46.0%) missing values | Missing |
CODEN has 10506 (86.2%) missing values | Missing |
PubMed ID has 11528 (94.6%) missing values | Missing |
Open Access has 8657 (71.0%) missing values | Missing |
doi_norm has 2068 (17.0%) missing values | Missing |
Cited by is highly skewed (γ1 = 49.58005625) | Skewed |
Link has unique values | Unique |
EID has unique values | Unique |
Cited by has 3335 (27.4%) zeros | Zeros |
Reproduction
| Analysis started | 2026-01-14 11:02:20.762980 |
|---|---|
| Analysis finished | 2026-01-14 11:02:48.602537 |
| Duration | 27.84 seconds |
| Software version | ydata-profiling vv4.18.1 |
| Download configuration | config.json |
Variables
Authors
Text
Missing
| Distinct | 10868 |
|---|---|
| Distinct (%) | 92.0% |
| Missing | 384 |
| Missing (%) | 3.1% |
| Memory size | 1.2 MiB |
Length
| Max length | 620 |
|---|---|
| Median length | 234 |
| Mean length | 41.251355 |
| Min length | 6 |
Unique
| Unique | 10225 ? |
|---|---|
| Unique (%) | 86.6% |
Sample
| 1st row | Wang, Y. |
|---|---|
| 2nd row | Lin, Y.; Zhang, Y.; Yang, Y.; Pan, S.; Ren, X.; Chen, D. |
| 3rd row | Hsu, T.-C.; Hsu, T.-P. |
| 4th row | Aksoy, B.D.; Mumcu, F.K.; Cantürk Günhan, B.C. |
| 5th row | van Bergen, R.; Huebotter, J.; A.; Lanillos, P. |
| Value | Count | Frequency (%) |
| m | 2633 | 3.4% |
| a | 2378 | 3.0% |
| s | 2225 | 2.8% |
| j | 2215 | 2.8% |
| c | 1461 | 1.9% |
| d | 1323 | 1.7% |
| r | 1252 | 1.6% |
| l | 1173 | 1.5% |
| y | 1164 | 1.5% |
| k | 1048 | 1.3% |
| Other values (17825) | 61208 |
Most occurring characters
| Value | Count | Frequency (%) |
| 66270 | 13.6% | |
| . | 50065 | 10.3% |
| , | 38180 | 7.8% |
| a | 29872 | 6.1% |
| ; | 26442 | 5.4% |
| e | 20717 | 4.3% |
| n | 19571 | 4.0% |
| i | 17642 | 3.6% |
| o | 16140 | 3.3% |
| r | 16098 | 3.3% |
| Other values (142) | 186099 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 487096 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 66270 | 13.6% | |
| . | 50065 | 10.3% |
| , | 38180 | 7.8% |
| a | 29872 | 6.1% |
| ; | 26442 | 5.4% |
| e | 20717 | 4.3% |
| n | 19571 | 4.0% |
| i | 17642 | 3.6% |
| o | 16140 | 3.3% |
| r | 16098 | 3.3% |
| Other values (142) | 186099 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 487096 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 66270 | 13.6% | |
| . | 50065 | 10.3% |
| , | 38180 | 7.8% |
| a | 29872 | 6.1% |
| ; | 26442 | 5.4% |
| e | 20717 | 4.3% |
| n | 19571 | 4.0% |
| i | 17642 | 3.6% |
| o | 16140 | 3.3% |
| r | 16098 | 3.3% |
| Other values (142) | 186099 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 487096 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 66270 | 13.6% | |
| . | 50065 | 10.3% |
| , | 38180 | 7.8% |
| a | 29872 | 6.1% |
| ; | 26442 | 5.4% |
| e | 20717 | 4.3% |
| n | 19571 | 4.0% |
| i | 17642 | 3.6% |
| o | 16140 | 3.3% |
| r | 16098 | 3.3% |
| Other values (142) | 186099 |
Author full names
Text
Missing
| Distinct | 10811 |
|---|---|
| Distinct (%) | 91.6% |
| Missing | 384 |
| Missing (%) | 3.1% |
| Memory size | 2.1 MiB |
Length
| Max length | 1511 |
|---|---|
| Median length | 447 |
| Mean length | 102.36247 |
| Min length | 19 |
Unique
| Unique | 10136 ? |
|---|---|
| Unique (%) | 85.8% |
Sample
| 1st row | Wang, Yang (57208730125) |
|---|---|
| 2nd row | Lin, Yuru (57281795200); Zhang, Yi (58957195500); Yang, Yuqin (57164390600); Pan, Shidan (60209651800); Ren, Xu (60209651900); Chen, Dengkang (57898076100) |
| 3rd row | Hsu, Tingchia (35173046500); Hsu, Taiping (58366049000) |
| 4th row | Aksoy, Behiye Dinçer (60177502400); Mumcu, Filiz Kuşkaya (13410584100); Cantürk Günhan, Berna (36815607700) |
| 5th row | van Bergen, Ruben S. (55502596000); Huebotter, Justus F. (57901993200); Lanillos, Pablo (24076529300) |
| Value | Count | Frequency (%) |
| a | 887 | 0.7% |
| m | 872 | 0.7% |
| j | 710 | 0.5% |
| wang | 523 | 0.4% |
| li | 460 | 0.4% |
| s | 458 | 0.3% |
| c | 457 | 0.3% |
| r | 448 | 0.3% |
| l | 431 | 0.3% |
| zhang | 430 | 0.3% |
| Other values (53187) | 125656 |
Most occurring characters
| Value | Count | Frequency (%) |
| 119520 | 9.9% | |
| 0 | 82349 | 6.8% |
| a | 63480 | 5.3% |
| 5 | 56726 | 4.7% |
| n | 43018 | 3.6% |
| 7 | 42759 | 3.5% |
| i | 42653 | 3.5% |
| e | 41241 | 3.4% |
| 2 | 39625 | 3.3% |
| ( | 38255 | 3.2% |
| Other values (168) | 639070 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 1208696 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 119520 | 9.9% | |
| 0 | 82349 | 6.8% |
| a | 63480 | 5.3% |
| 5 | 56726 | 4.7% |
| n | 43018 | 3.6% |
| 7 | 42759 | 3.5% |
| i | 42653 | 3.5% |
| e | 41241 | 3.4% |
| 2 | 39625 | 3.3% |
| ( | 38255 | 3.2% |
| Other values (168) | 639070 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 1208696 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 119520 | 9.9% | |
| 0 | 82349 | 6.8% |
| a | 63480 | 5.3% |
| 5 | 56726 | 4.7% |
| n | 43018 | 3.6% |
| 7 | 42759 | 3.5% |
| i | 42653 | 3.5% |
| e | 41241 | 3.4% |
| 2 | 39625 | 3.3% |
| ( | 38255 | 3.2% |
| Other values (168) | 639070 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 1208696 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 119520 | 9.9% | |
| 0 | 82349 | 6.8% |
| a | 63480 | 5.3% |
| 5 | 56726 | 4.7% |
| n | 43018 | 3.6% |
| 7 | 42759 | 3.5% |
| i | 42653 | 3.5% |
| e | 41241 | 3.4% |
| 2 | 39625 | 3.3% |
| ( | 38255 | 3.2% |
| Other values (168) | 639070 |
Author(s) ID
Text
Missing
| Distinct | 10811 |
|---|---|
| Distinct (%) | 91.6% |
| Missing | 384 |
| Missing (%) | 3.1% |
| Memory size | 1.0 MiB |
Length
| Max length | 604 |
|---|---|
| Median length | 430 |
| Mean length | 39.570461 |
| Min length | 10 |
Unique
| Unique | 10136 ? |
|---|---|
| Unique (%) | 85.8% |
Sample
| 1st row | 57208730125 |
|---|---|
| 2nd row | 57281795200; 58957195500; 57164390600; 60209651800; 60209651900; 57898076100 |
| 3rd row | 35173046500; 58366049000 |
| 4th row | 60177502400; 13410584100; 36815607700 |
| 5th row | 55502596000; 57901993200; 60247114700; 24076529300 |
| Value | Count | Frequency (%) |
| 6507819920 | 50 | 0.1% |
| 9638194400 | 50 | 0.1% |
| 35103870600 | 47 | 0.1% |
| 23096666800 | 46 | 0.1% |
| 57211726890 | 45 | 0.1% |
| 6701492423 | 42 | 0.1% |
| 35173046500 | 41 | 0.1% |
| 6701594126 | 41 | 0.1% |
| 7203044800 | 39 | 0.1% |
| 8286496000 | 39 | 0.1% |
| Other values (25736) | 37810 |
Most occurring characters
| Value | Count | Frequency (%) |
| 0 | 82379 | |
| 5 | 56733 | |
| 7 | 42768 | |
| 2 | 39643 | |
| 6 | 35999 | |
| 1 | 35455 | |
| 9 | 31345 | 6.7% |
| 3 | 31326 | 6.7% |
| 4 | 29614 | 6.3% |
| 8 | 29102 | 6.2% |
| Other values (2) | 52884 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 467248 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 0 | 82379 | |
| 5 | 56733 | |
| 7 | 42768 | |
| 2 | 39643 | |
| 6 | 35999 | |
| 1 | 35455 | |
| 9 | 31345 | 6.7% |
| 3 | 31326 | 6.7% |
| 4 | 29614 | 6.3% |
| 8 | 29102 | 6.2% |
| Other values (2) | 52884 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 467248 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 0 | 82379 | |
| 5 | 56733 | |
| 7 | 42768 | |
| 2 | 39643 | |
| 6 | 35999 | |
| 1 | 35455 | |
| 9 | 31345 | 6.7% |
| 3 | 31326 | 6.7% |
| 4 | 29614 | 6.3% |
| 8 | 29102 | 6.2% |
| Other values (2) | 52884 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 467248 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 0 | 82379 | |
| 5 | 56733 | |
| 7 | 42768 | |
| 2 | 39643 | |
| 6 | 35999 | |
| 1 | 35455 | |
| 9 | 31345 | 6.7% |
| 3 | 31326 | 6.7% |
| 4 | 29614 | 6.3% |
| 8 | 29102 | 6.2% |
| Other values (2) | 52884 |
Title
Text
| Distinct | 12076 |
|---|---|
| Distinct (%) | 99.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 2.0 MiB |
Length
| Max length | 476 |
|---|---|
| Median length | 263 |
| Mean length | 92.691929 |
| Min length | 6 |
Unique
| Unique | 11997 ? |
|---|---|
| Unique (%) | 98.4% |
Sample
| 1st row | Effects of troubleshooting robotics learning on students’ engagement, computational thinking, and programming skills |
|---|---|
| 2nd row | Facilitating computational thinking with AI: A three-level meta-analytic evidence for future-ready learning |
| 3rd row | Effects of game-based learning integrated with different thinking-guided methods on computational thinking of elementary school students |
| 4th row | Unveiling the nexus: Computational thinking and mathematical modelling in K-12 education- a teacher-centric exploration |
| 5th row | Object-centric proto-symbolic behavioural reasoning from pixels |
| Value | Count | Frequency (%) |
| of | 5923 | 4.1% |
| and | 5369 | 3.7% |
| in | 4968 | 3.4% |
| computational | 4947 | 3.4% |
| thinking | 4866 | 3.3% |
| the | 4180 | 2.9% |
| a | 3874 | 2.7% |
| for | 2934 | 2.0% |
| to | 2151 | 1.5% |
| on | 1951 | 1.3% |
| Other values (14201) | 104680 |
Most occurring characters
| Value | Count | Frequency (%) |
| 133418 | 11.8% | |
| n | 89717 | 7.9% |
| i | 88928 | 7.9% |
| e | 85590 | 7.6% |
| t | 74627 | 6.6% |
| o | 74583 | 6.6% |
| a | 74075 | 6.6% |
| r | 51114 | 4.5% |
| s | 45739 | 4.0% |
| l | 38884 | 3.4% |
| Other values (736) | 373425 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 1130100 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 133418 | 11.8% | |
| n | 89717 | 7.9% |
| i | 88928 | 7.9% |
| e | 85590 | 7.6% |
| t | 74627 | 6.6% |
| o | 74583 | 6.6% |
| a | 74075 | 6.6% |
| r | 51114 | 4.5% |
| s | 45739 | 4.0% |
| l | 38884 | 3.4% |
| Other values (736) | 373425 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 1130100 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 133418 | 11.8% | |
| n | 89717 | 7.9% |
| i | 88928 | 7.9% |
| e | 85590 | 7.6% |
| t | 74627 | 6.6% |
| o | 74583 | 6.6% |
| a | 74075 | 6.6% |
| r | 51114 | 4.5% |
| s | 45739 | 4.0% |
| l | 38884 | 3.4% |
| Other values (736) | 373425 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 1130100 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 133418 | 11.8% | |
| n | 89717 | 7.9% |
| i | 88928 | 7.9% |
| e | 85590 | 7.6% |
| t | 74627 | 6.6% |
| o | 74583 | 6.6% |
| a | 74075 | 6.6% |
| r | 51114 | 4.5% |
| s | 45739 | 4.0% |
| l | 38884 | 3.4% |
| Other values (736) | 373425 |
Year
Real number (ℝ)
High correlation
| Distinct | 50 |
|---|---|
| Distinct (%) | 0.4% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 2018.948 |
| Minimum | 1970 |
|---|---|
| Maximum | 2026 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 95.4 KiB |
Quantile statistics
| Minimum | 1970 |
|---|---|
| 5-th percentile | 2006 |
| Q1 | 2017 |
| median | 2021 |
| Q3 | 2023 |
| 95-th percentile | 2025 |
| Maximum | 2026 |
| Range | 56 |
| Interquartile range (IQR) | 6 |
Descriptive statistics
| Standard deviation | 6.3713909 |
|---|---|
| Coefficient of variation (CV) | 0.0031557974 |
| Kurtosis | 5.4101335 |
| Mean | 2018.948 |
| Median Absolute Deviation (MAD) | 3 |
| Skewness | -2.0310781 |
| Sum | 24615014 |
| Variance | 40.594622 |
| Monotonicity | Decreasing |
| Value | Count | Frequency (%) |
| 2024 | 1445 | |
| 2025 | 1331 | |
| 2023 | 1289 | |
| 2022 | 1129 | |
| 2021 | 1056 | |
| 2020 | 974 | 8.0% |
| 2019 | 868 | 7.1% |
| 2018 | 667 | 5.5% |
| 2017 | 560 | 4.6% |
| 2016 | 384 | 3.1% |
| Other values (40) | 2489 |
| Value | Count | Frequency (%) |
| 1970 | 1 | < 0.1% |
| 1975 | 1 | < 0.1% |
| 1977 | 1 | < 0.1% |
| 1980 | 1 | < 0.1% |
| 1981 | 1 | < 0.1% |
| 1982 | 1 | < 0.1% |
| 1983 | 5 | |
| 1984 | 7 | |
| 1985 | 1 | < 0.1% |
| 1986 | 5 |
| Value | Count | Frequency (%) |
| 2026 | 68 | 0.6% |
| 2025 | 1331 | |
| 2024 | 1445 | |
| 2023 | 1289 | |
| 2022 | 1129 | |
| 2021 | 1056 | |
| 2020 | 974 | |
| 2019 | 868 | |
| 2018 | 667 | |
| 2017 | 560 | 4.6% |
Source title
Text
Missing
| Distinct | 2267 |
|---|---|
| Distinct (%) | 25.6% |
| Missing | 3321 |
| Missing (%) | 27.2% |
| Memory size | 884.8 KiB |
Length
| Max length | 158 |
|---|---|
| Median length | 93 |
| Mean length | 41.140458 |
| Min length | 3 |
Unique
| Unique | 1424 ? |
|---|---|
| Unique (%) | 16.1% |
Sample
| 1st row | Thinking Skills and Creativity |
|---|---|
| 2nd row | Thinking Skills and Creativity |
| 3rd row | Thinking Skills and Creativity |
| 4th row | Thinking Skills and Creativity |
| 5th row | Neural Networks |
| Value | Count | Frequency (%) |
| of | 2817 | 6.2% |
| and | 2580 | 5.7% |
| in | 2237 | 4.9% |
| conference | 2159 | 4.8% |
| education | 2127 | 4.7% |
| journal | 1765 | 3.9% |
| international | 1660 | 3.7% |
| proceedings | 1526 | 3.4% |
| science | 1376 | 3.0% |
| computer | 1106 | 2.4% |
| Other values (1847) | 26001 |
Most occurring characters
| Value | Count | Frequency (%) |
| n | 38658 | 10.6% |
| 36483 | 10.0% | |
| e | 34778 | 9.5% |
| o | 28604 | 7.8% |
| i | 25853 | 7.1% |
| a | 21849 | 6.0% |
| t | 19117 | 5.2% |
| r | 18414 | 5.0% |
| c | 18142 | 5.0% |
| s | 11549 | 3.2% |
| Other values (59) | 111510 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 364957 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| n | 38658 | 10.6% |
| 36483 | 10.0% | |
| e | 34778 | 9.5% |
| o | 28604 | 7.8% |
| i | 25853 | 7.1% |
| a | 21849 | 6.0% |
| t | 19117 | 5.2% |
| r | 18414 | 5.0% |
| c | 18142 | 5.0% |
| s | 11549 | 3.2% |
| Other values (59) | 111510 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 364957 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| n | 38658 | 10.6% |
| 36483 | 10.0% | |
| e | 34778 | 9.5% |
| o | 28604 | 7.8% |
| i | 25853 | 7.1% |
| a | 21849 | 6.0% |
| t | 19117 | 5.2% |
| r | 18414 | 5.0% |
| c | 18142 | 5.0% |
| s | 11549 | 3.2% |
| Other values (59) | 111510 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 364957 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| n | 38658 | 10.6% |
| 36483 | 10.0% | |
| e | 34778 | 9.5% |
| o | 28604 | 7.8% |
| i | 25853 | 7.1% |
| a | 21849 | 6.0% |
| t | 19117 | 5.2% |
| r | 18414 | 5.0% |
| c | 18142 | 5.0% |
| s | 11549 | 3.2% |
| Other values (59) | 111510 |
Volume
Text
Missing
| Distinct | 1338 |
|---|---|
| Distinct (%) | 16.8% |
| Missing | 4208 |
| Missing (%) | 34.5% |
| Memory size | 540.2 KiB |
Length
| Max length | 69 |
|---|---|
| Median length | 2 |
| Mean length | 3.3999248 |
| Min length | 1 |
Unique
| Unique | 809 ? |
|---|---|
| Unique (%) | 10.1% |
Sample
| 1st row | 60 |
|---|---|
| 2nd row | 60 |
| 3rd row | 60 |
| 4th row | 60 |
| 5th row | 197 |
| Value | Count | Frequency (%) |
| 2 | 310 | 3.5% |
| lncs | 306 | 3.5% |
| 1 | 284 | 3.2% |
| 13 | 182 | 2.1% |
| 14 | 164 | 1.9% |
| 15 | 162 | 1.9% |
| 12 | 154 | 1.8% |
| 10 | 153 | 1.7% |
| 11 | 140 | 1.6% |
| 29 | 138 | 1.6% |
| Other values (1313) | 6755 |
Most occurring characters
| Value | Count | Frequency (%) |
| 1 | 4161 | |
| 2 | 3525 | |
| 3 | 2023 | 7.5% |
| 0 | 1832 | 6.7% |
| 4 | 1542 | 5.7% |
| 5 | 1506 | 5.5% |
| 6 | 1276 | 4.7% |
| 9 | 1248 | 4.6% |
| 8 | 1247 | 4.6% |
| 7 | 1122 | 4.1% |
| Other values (44) | 7663 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 27145 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 1 | 4161 | |
| 2 | 3525 | |
| 3 | 2023 | 7.5% |
| 0 | 1832 | 6.7% |
| 4 | 1542 | 5.7% |
| 5 | 1506 | 5.5% |
| 6 | 1276 | 4.7% |
| 9 | 1248 | 4.6% |
| 8 | 1247 | 4.6% |
| 7 | 1122 | 4.1% |
| Other values (44) | 7663 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 27145 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 1 | 4161 | |
| 2 | 3525 | |
| 3 | 2023 | 7.5% |
| 0 | 1832 | 6.7% |
| 4 | 1542 | 5.7% |
| 5 | 1506 | 5.5% |
| 6 | 1276 | 4.7% |
| 9 | 1248 | 4.6% |
| 8 | 1247 | 4.6% |
| 7 | 1122 | 4.1% |
| Other values (44) | 7663 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 27145 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 1 | 4161 | |
| 2 | 3525 | |
| 3 | 2023 | 7.5% |
| 0 | 1832 | 6.7% |
| 4 | 1542 | 5.7% |
| 5 | 1506 | 5.5% |
| 6 | 1276 | 4.7% |
| 9 | 1248 | 4.6% |
| 8 | 1247 | 4.6% |
| 7 | 1122 | 4.1% |
| Other values (44) | 7663 |
Issue
Text
Missing
| Distinct | 250 |
|---|---|
| Distinct (%) | 5.5% |
| Missing | 7629 |
| Missing (%) | 62.6% |
| Memory size | 463.9 KiB |
Length
| Max length | 81 |
|---|---|
| Median length | 1 |
| Mean length | 1.5844839 |
| Min length | 1 |
Unique
| Unique | 159 ? |
|---|---|
| Unique (%) | 3.5% |
Sample
| 1st row | 1 |
|---|---|
| 2nd row | 1 |
| 3rd row | 2 |
| 4th row | 1 |
| 5th row | 2 |
| Value | Count | Frequency (%) |
| 1 | 972 | |
| 2 | 747 | |
| 3 | 581 | |
| 4 | 545 | |
| 6 | 273 | 5.8% |
| 5 | 261 | 5.5% |
| 7 | 117 | 2.5% |
| 9 | 115 | 2.4% |
| 8 | 106 | 2.2% |
| 11 | 88 | 1.9% |
| Other values (241) | 939 |
Most occurring characters
| Value | Count | Frequency (%) |
| 1 | 1591 | |
| 2 | 1080 | |
| 3 | 713 | |
| 4 | 639 | 8.8% |
| 6 | 368 | 5.1% |
| 5 | 362 | 5.0% |
| 8 | 204 | 2.8% |
| 7 | 196 | 2.7% |
| 181 | 2.5% | |
| 9 | 160 | 2.2% |
| Other values (52) | 1736 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 7230 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 1 | 1591 | |
| 2 | 1080 | |
| 3 | 713 | |
| 4 | 639 | 8.8% |
| 6 | 368 | 5.1% |
| 5 | 362 | 5.0% |
| 8 | 204 | 2.8% |
| 7 | 196 | 2.7% |
| 181 | 2.5% | |
| 9 | 160 | 2.2% |
| Other values (52) | 1736 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 7230 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 1 | 1591 | |
| 2 | 1080 | |
| 3 | 713 | |
| 4 | 639 | 8.8% |
| 6 | 368 | 5.1% |
| 5 | 362 | 5.0% |
| 8 | 204 | 2.8% |
| 7 | 196 | 2.7% |
| 181 | 2.5% | |
| 9 | 160 | 2.2% |
| Other values (52) | 1736 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 7230 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 1 | 1591 | |
| 2 | 1080 | |
| 3 | 713 | |
| 4 | 639 | 8.8% |
| 6 | 368 | 5.1% |
| 5 | 362 | 5.0% |
| 8 | 204 | 2.8% |
| 7 | 196 | 2.7% |
| 181 | 2.5% | |
| 9 | 160 | 2.2% |
| Other values (52) | 1736 |
Art. No.
Text
Missing
| Distinct | 2309 |
|---|---|
| Distinct (%) | 88.7% |
| Missing | 9589 |
| Missing (%) | 78.6% |
| Memory size | 438.6 KiB |
Length
| Max length | 19 |
|---|---|
| Median length | 18 |
| Mean length | 5.6131387 |
| Min length | 1 |
Unique
| Unique | 2183 ? |
|---|---|
| Unique (%) | 83.9% |
Sample
| 1st row | 102068 |
|---|---|
| 2nd row | 102070 |
| 3rd row | 102056 |
| 4th row | 102049 |
| 5th row | 108407 |
| Value | Count | Frequency (%) |
| 11 | 11 | 0.4% |
| 7 | 10 | 0.4% |
| 1 | 10 | 0.4% |
| 3 | 10 | 0.4% |
| 4 | 10 | 0.4% |
| 10 | 9 | 0.3% |
| 6 | 9 | 0.3% |
| 19 | 8 | 0.3% |
| 2 | 8 | 0.3% |
| 13 | 7 | 0.3% |
| Other values (2302) | 2515 |
Most occurring characters
| Value | Count | Frequency (%) |
| 0 | 2192 | |
| 1 | 2142 | |
| 2 | 1493 | |
| 3 | 1281 | |
| 4 | 1261 | |
| 5 | 1200 | |
| 9 | 1170 | |
| 7 | 1137 | |
| 8 | 1128 | |
| 6 | 1121 | |
| Other values (46) | 486 | 3.3% |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 14611 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 0 | 2192 | |
| 1 | 2142 | |
| 2 | 1493 | |
| 3 | 1281 | |
| 4 | 1261 | |
| 5 | 1200 | |
| 9 | 1170 | |
| 7 | 1137 | |
| 8 | 1128 | |
| 6 | 1121 | |
| Other values (46) | 486 | 3.3% |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 14611 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 0 | 2192 | |
| 1 | 2142 | |
| 2 | 1493 | |
| 3 | 1281 | |
| 4 | 1261 | |
| 5 | 1200 | |
| 9 | 1170 | |
| 7 | 1137 | |
| 8 | 1128 | |
| 6 | 1121 | |
| Other values (46) | 486 | 3.3% |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 14611 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 0 | 2192 | |
| 1 | 2142 | |
| 2 | 1493 | |
| 3 | 1281 | |
| 4 | 1261 | |
| 5 | 1200 | |
| 9 | 1170 | |
| 7 | 1137 | |
| 8 | 1128 | |
| 6 | 1121 | |
| Other values (46) | 486 | 3.3% |
Page start
Text
Missing
| Distinct | 2158 |
|---|---|
| Distinct (%) | 23.9% |
| Missing | 3148 |
| Missing (%) | 25.8% |
| Memory size | 556.4 KiB |
Length
| Max length | 10 |
|---|---|
| Median length | 3 |
| Mean length | 2.841663 |
| Min length | 1 |
Unique
| Unique | 1129 ? |
|---|---|
| Unique (%) | 12.5% |
Sample
| 1st row | 126 |
|---|---|
| 2nd row | 208 |
| 3rd row | 36 |
| 4th row | 154 |
| 5th row | 213 |
| Value | Count | Frequency (%) |
| 1 | 467 | 5.2% |
| 3 | 53 | 0.6% |
| 77 | 37 | 0.4% |
| 63 | 34 | 0.4% |
| 57 | 33 | 0.4% |
| 35 | 32 | 0.4% |
| 21 | 32 | 0.4% |
| 113 | 32 | 0.4% |
| 213 | 32 | 0.4% |
| 9 | 31 | 0.3% |
| Other values (2148) | 8261 |
Most occurring characters
| Value | Count | Frequency (%) |
| 1 | 5082 | |
| 3 | 3110 | |
| 2 | 3046 | |
| 5 | 2505 | |
| 4 | 2286 | |
| 7 | 2216 | |
| 9 | 2089 | |
| 6 | 1973 | 7.7% |
| 8 | 1772 | 6.9% |
| 0 | 1563 | 6.1% |
| Other values (18) | 58 | 0.2% |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 25700 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 1 | 5082 | |
| 3 | 3110 | |
| 2 | 3046 | |
| 5 | 2505 | |
| 4 | 2286 | |
| 7 | 2216 | |
| 9 | 2089 | |
| 6 | 1973 | 7.7% |
| 8 | 1772 | 6.9% |
| 0 | 1563 | 6.1% |
| Other values (18) | 58 | 0.2% |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 25700 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 1 | 5082 | |
| 3 | 3110 | |
| 2 | 3046 | |
| 5 | 2505 | |
| 4 | 2286 | |
| 7 | 2216 | |
| 9 | 2089 | |
| 6 | 1973 | 7.7% |
| 8 | 1772 | 6.9% |
| 0 | 1563 | 6.1% |
| Other values (18) | 58 | 0.2% |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 25700 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 1 | 5082 | |
| 3 | 3110 | |
| 2 | 3046 | |
| 5 | 2505 | |
| 4 | 2286 | |
| 7 | 2216 | |
| 9 | 2089 | |
| 6 | 1973 | 7.7% |
| 8 | 1772 | 6.9% |
| 0 | 1563 | 6.1% |
| Other values (18) | 58 | 0.2% |
Page end
Text
Missing
| Distinct | 2164 |
|---|---|
| Distinct (%) | 24.4% |
| Missing | 3305 |
| Missing (%) | 27.1% |
| Memory size | 554.2 KiB |
Length
| Max length | 7 |
|---|---|
| Median length | 3 |
| Mean length | 2.9447508 |
| Min length | 1 |
Unique
| Unique | 1119 ? |
|---|---|
| Unique (%) | 12.6% |
Sample
| 1st row | 154 |
|---|---|
| 2nd row | 237 |
| 3rd row | 51 |
| 4th row | 164 |
| 5th row | 240 |
| Value | Count | Frequency (%) |
| 17 | 39 | 0.4% |
| 29 | 31 | 0.3% |
| 27 | 29 | 0.3% |
| 24 | 29 | 0.3% |
| 72 | 29 | 0.3% |
| 28 | 29 | 0.3% |
| 76 | 28 | 0.3% |
| 18 | 28 | 0.3% |
| 62 | 28 | 0.3% |
| 71 | 27 | 0.3% |
| Other values (2154) | 8590 |
Most occurring characters
| Value | Count | Frequency (%) |
| 1 | 4413 | |
| 2 | 3584 | |
| 3 | 2769 | |
| 4 | 2653 | |
| 6 | 2392 | |
| 5 | 2324 | |
| 8 | 2091 | |
| 7 | 2007 | |
| 0 | 1947 | |
| 9 | 1938 | |
| Other values (18) | 52 | 0.2% |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 26170 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 1 | 4413 | |
| 2 | 3584 | |
| 3 | 2769 | |
| 4 | 2653 | |
| 6 | 2392 | |
| 5 | 2324 | |
| 8 | 2091 | |
| 7 | 2007 | |
| 0 | 1947 | |
| 9 | 1938 | |
| Other values (18) | 52 | 0.2% |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 26170 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 1 | 4413 | |
| 2 | 3584 | |
| 3 | 2769 | |
| 4 | 2653 | |
| 6 | 2392 | |
| 5 | 2324 | |
| 8 | 2091 | |
| 7 | 2007 | |
| 0 | 1947 | |
| 9 | 1938 | |
| Other values (18) | 52 | 0.2% |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 26170 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 1 | 4413 | |
| 2 | 3584 | |
| 3 | 2769 | |
| 4 | 2653 | |
| 6 | 2392 | |
| 5 | 2324 | |
| 8 | 2091 | |
| 7 | 2007 | |
| 0 | 1947 | |
| 9 | 1938 | |
| Other values (18) | 52 | 0.2% |
Cited by
Real number (ℝ)
Skewed Zeros
| Distinct | 314 |
|---|---|
| Distinct (%) | 2.6% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 18.663058 |
| Minimum | 0 |
|---|---|
| Maximum | 10043 |
| Zeros | 3335 |
| Zeros (%) | 27.4% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 95.4 KiB |
Quantile statistics
| Minimum | 0 |
|---|---|
| 5-th percentile | 0 |
| Q1 | 0 |
| median | 3 |
| Q3 | 12 |
| 95-th percentile | 68 |
| Maximum | 10043 |
| Range | 10043 |
| Interquartile range (IQR) | 12 |
Descriptive statistics
| Standard deviation | 128.52238 |
|---|---|
| Coefficient of variation (CV) | 6.886459 |
| Kurtosis | 3386.3971 |
| Mean | 18.663058 |
| Median Absolute Deviation (MAD) | 3 |
| Skewness | 49.580056 |
| Sum | 227540 |
| Variance | 16518.003 |
| Monotonicity | Not monotonic |
| Value | Count | Frequency (%) |
| 0 | 3335 | |
| 1 | 1497 | |
| 2 | 980 | 8.0% |
| 3 | 726 | 6.0% |
| 4 | 559 | 4.6% |
| 5 | 429 | 3.5% |
| 6 | 349 | 2.9% |
| 7 | 300 | 2.5% |
| 8 | 286 | 2.3% |
| 9 | 248 | 2.0% |
| Other values (304) | 3483 |
| Value | Count | Frequency (%) |
| 0 | 3335 | |
| 1 | 1497 | |
| 2 | 980 | 8.0% |
| 3 | 726 | 6.0% |
| 4 | 559 | 4.6% |
| 5 | 429 | 3.5% |
| 6 | 349 | 2.9% |
| 7 | 300 | 2.5% |
| 8 | 286 | 2.3% |
| 9 | 248 | 2.0% |
| Value | Count | Frequency (%) |
| 10043 | 1 | |
| 5509 | 1 | |
| 3161 | 1 | |
| 3156 | 1 | |
| 2123 | 1 | |
| 1908 | 1 | |
| 1703 | 1 | |
| 1438 | 1 | |
| 1317 | 1 | |
| 1306 | 1 |
DOI
Text
Missing
| Distinct | 10106 |
|---|---|
| Distinct (%) | 99.8% |
| Missing | 2068 |
| Missing (%) | 17.0% |
| Memory size | 801.5 KiB |
Length
| Max length | 66 |
|---|---|
| Median length | 58 |
| Mean length | 25.522916 |
| Min length | 12 |
Unique
| Unique | 10088 ? |
|---|---|
| Unique (%) | 99.6% |
Sample
| 1st row | 10.1016/j.tsc.2025.102068 |
|---|---|
| 2nd row | 10.1016/j.tsc.2025.102070 |
| 3rd row | 10.1016/j.tsc.2025.102056 |
| 4th row | 10.1016/j.tsc.2025.102049 |
| 5th row | 10.1016/j.neunet.2025.108407 |
| Value | Count | Frequency (%) |
| 10.1007/s11423-023-10328-8 | 2 | < 0.1% |
| 10.1051/e3sconf/202453805034 | 2 | < 0.1% |
| 10.1145/1140124.1140161 | 2 | < 0.1% |
| 10.1016/j.procir.2024.10.161 | 2 | < 0.1% |
| 10.1145/3159450.3159586 | 2 | < 0.1% |
| 10.1016/b978-0-12-809324-5.23765-6 | 2 | < 0.1% |
| 10.1016/b978-044451719-7/50072-x | 2 | < 0.1% |
| 10.1016/b978-0-12-804071-3.00012-4 | 2 | < 0.1% |
| 10.4324/9781351232357 | 2 | < 0.1% |
| 10.34190/gbl.20.156 | 2 | < 0.1% |
| Other values (10096) | 10104 |
Most occurring characters
| Value | Count | Frequency (%) |
| 1 | 41207 | |
| 0 | 40154 | |
| . | 22455 | 8.7% |
| 2 | 18979 | 7.3% |
| 3 | 15621 | 6.0% |
| 9 | 13345 | 5.2% |
| 7 | 12030 | 4.7% |
| 4 | 11597 | 4.5% |
| 5 | 11450 | 4.4% |
| 8 | 11024 | 4.3% |
| Other values (64) | 60532 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 258394 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 1 | 41207 | |
| 0 | 40154 | |
| . | 22455 | 8.7% |
| 2 | 18979 | 7.3% |
| 3 | 15621 | 6.0% |
| 9 | 13345 | 5.2% |
| 7 | 12030 | 4.7% |
| 4 | 11597 | 4.5% |
| 5 | 11450 | 4.4% |
| 8 | 11024 | 4.3% |
| Other values (64) | 60532 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 258394 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 1 | 41207 | |
| 0 | 40154 | |
| . | 22455 | 8.7% |
| 2 | 18979 | 7.3% |
| 3 | 15621 | 6.0% |
| 9 | 13345 | 5.2% |
| 7 | 12030 | 4.7% |
| 4 | 11597 | 4.5% |
| 5 | 11450 | 4.4% |
| 8 | 11024 | 4.3% |
| Other values (64) | 60532 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 258394 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 1 | 41207 | |
| 0 | 40154 | |
| . | 22455 | 8.7% |
| 2 | 18979 | 7.3% |
| 3 | 15621 | 6.0% |
| 9 | 13345 | 5.2% |
| 7 | 12030 | 4.7% |
| 4 | 11597 | 4.5% |
| 5 | 11450 | 4.4% |
| 8 | 11024 | 4.3% |
| Other values (64) | 60532 |
Link
URL
Unique
| Distinct | 12192 |
|---|---|
| Distinct (%) | 100.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 2.2 MiB |
| https://www.scopus.com/inward/record.uri?eid=2-s2.0-77956727537&doi=10.1016%2FS0166-4115%2808%2962638-2&partnerID=40&md5=fd488215e393375baaef12aa42725781 | 1 |
|---|---|
| https://www.scopus.com/inward/record.uri?eid=2-s2.0-0021727627&partnerID=40&md5=be2dc96ebd9234e597d319116bb12972 | 1 |
| https://www.scopus.com/inward/record.uri?eid=2-s2.0-0022320446&partnerID=40&md5=aeb5b56ef783c91f87cacc651e50b261 | 1 |
| https://www.scopus.com/inward/record.uri?eid=2-s2.0-84990557138&doi=10.1111%2Fj.1467-8640.1986.tb00069.x&partnerID=40&md5=b2a6bd5a8668ee647e8ce864f55cb012 | 1 |
| https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168894514&partnerID=40&md5=5763976e0aa6d840c46ea85e4cb077a3 | 1 |
| Other values (12187) |
| Value | Count | Frequency (%) |
| https://www.scopus.com/inward/record.uri?eid=2-s2.0-77956727537&doi=10.1016%2FS0166-4115%2808%2962638-2&partnerID=40&md5=fd488215e393375baaef12aa42725781 | 1 | < 0.1% |
| https://www.scopus.com/inward/record.uri?eid=2-s2.0-0021727627&partnerID=40&md5=be2dc96ebd9234e597d319116bb12972 | 1 | < 0.1% |
| https://www.scopus.com/inward/record.uri?eid=2-s2.0-0022320446&partnerID=40&md5=aeb5b56ef783c91f87cacc651e50b261 | 1 | < 0.1% |
| https://www.scopus.com/inward/record.uri?eid=2-s2.0-84990557138&doi=10.1111%2Fj.1467-8640.1986.tb00069.x&partnerID=40&md5=b2a6bd5a8668ee647e8ce864f55cb012 | 1 | < 0.1% |
| https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168894514&partnerID=40&md5=5763976e0aa6d840c46ea85e4cb077a3 | 1 | < 0.1% |
| https://www.scopus.com/inward/record.uri?eid=2-s2.0-0012101234&doi=10.1007%2FBF00051821&partnerID=40&md5=905960b3eac253fbdd5fc77151eccbe2 | 1 | < 0.1% |
| https://www.scopus.com/inward/record.uri?eid=2-s2.0-0012587120&doi=10.1145%2F6592.214913&partnerID=40&md5=a02ed9e48a4616e58d88191418edb778 | 1 | < 0.1% |
| https://www.scopus.com/inward/record.uri?eid=2-s2.0-0022959691&partnerID=40&md5=491fb3b0be55e6221fbd1520bc290595 | 1 | < 0.1% |
| https://www.scopus.com/inward/record.uri?eid=2-s2.0-84928454782&doi=10.1080%2F00201748708602112&partnerID=40&md5=c9a3342d0d441146ba199cab5ebcdb26 | 1 | < 0.1% |
| https://www.scopus.com/inward/record.uri?eid=2-s2.0-84950876142&doi=10.1080%2F02693798708927821&partnerID=40&md5=2ea5dc98ab5770c4303d68a60e662d8f | 1 | < 0.1% |
| Other values (12182) | 12182 |
| Value | Count | Frequency (%) |
| https | 12192 |
| Value | Count | Frequency (%) |
| www.scopus.com | 12192 |
| Value | Count | Frequency (%) |
| /inward/record.uri | 12192 |
| Value | Count | Frequency (%) |
| eid=2-s2.0-77956727537&doi=10.1016%2FS0166-4115%2808%2962638-2&partnerID=40&md5=fd488215e393375baaef12aa42725781 | 1 | < 0.1% |
| eid=2-s2.0-0021727627&partnerID=40&md5=be2dc96ebd9234e597d319116bb12972 | 1 | < 0.1% |
| eid=2-s2.0-0022320446&partnerID=40&md5=aeb5b56ef783c91f87cacc651e50b261 | 1 | < 0.1% |
| eid=2-s2.0-84990557138&doi=10.1111%2Fj.1467-8640.1986.tb00069.x&partnerID=40&md5=b2a6bd5a8668ee647e8ce864f55cb012 | 1 | < 0.1% |
| eid=2-s2.0-85168894514&partnerID=40&md5=5763976e0aa6d840c46ea85e4cb077a3 | 1 | < 0.1% |
| eid=2-s2.0-0012101234&doi=10.1007%2FBF00051821&partnerID=40&md5=905960b3eac253fbdd5fc77151eccbe2 | 1 | < 0.1% |
| eid=2-s2.0-0012587120&doi=10.1145%2F6592.214913&partnerID=40&md5=a02ed9e48a4616e58d88191418edb778 | 1 | < 0.1% |
| eid=2-s2.0-0022959691&partnerID=40&md5=491fb3b0be55e6221fbd1520bc290595 | 1 | < 0.1% |
| eid=2-s2.0-84928454782&doi=10.1080%2F00201748708602112&partnerID=40&md5=c9a3342d0d441146ba199cab5ebcdb26 | 1 | < 0.1% |
| eid=2-s2.0-84950876142&doi=10.1080%2F02693798708927821&partnerID=40&md5=2ea5dc98ab5770c4303d68a60e662d8f | 1 | < 0.1% |
| Other values (12182) | 12182 |
| Value | Count | Frequency (%) |
| 12192 |
Affiliations
Text
Missing
| Distinct | 9329 |
|---|---|
| Distinct (%) | 80.2% |
| Missing | 558 |
| Missing (%) | 4.6% |
| Memory size | 2.8 MiB |
Length
| Max length | 2979 |
|---|---|
| Median length | 628 |
| Mean length | 149.5618 |
| Min length | 6 |
Unique
| Unique | 8288 ? |
|---|---|
| Unique (%) | 71.2% |
Sample
| 1st row | Nanjing Normal University, Nanjing, Jiangsu, China |
|---|---|
| 2nd row | Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, China |
| 3rd row | Department of Technology Application and Human Resource Development, National Taiwan Normal University, Taipei, Taiwan |
| 4th row | General Directorate of Innovation and Educational Technologies, Ankara, Turkey; Department of Early Childhood Education, Universität Graz, Graz, Styria, Austria; Department of Mathematics and Science Education, Dokuz Eylül Üniversitesi, Izmir, Turkey |
| 5th row | Radboud Universiteit, Nijmegen, Gelderland, Netherlands; Cajal International Center for Neuroscience, Consejo Superior de Investigaciones Científicas, Madrid, Madrid, Spain |
| Value | Count | Frequency (%) |
| of | 16447 | 7.5% |
| university | 11348 | 5.2% |
| united | 7838 | 3.6% |
| states | 6817 | 3.1% |
| department | 5587 | 2.6% |
| and | 4993 | 2.3% |
| de | 2902 | 1.3% |
| education | 2782 | 1.3% |
| china | 2570 | 1.2% |
| science | 2559 | 1.2% |
| Other values (10094) | 155239 |
Most occurring characters
| Value | Count | Frequency (%) |
| 207445 | 11.9% | |
| e | 139230 | 8.0% |
| n | 127950 | 7.4% |
| i | 124838 | 7.2% |
| a | 123277 | 7.1% |
| t | 106130 | 6.1% |
| o | 91692 | 5.3% |
| , | 77281 | 4.4% |
| r | 76086 | 4.4% |
| s | 60614 | 3.5% |
| Other values (165) | 605459 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 1740002 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 207445 | 11.9% | |
| e | 139230 | 8.0% |
| n | 127950 | 7.4% |
| i | 124838 | 7.2% |
| a | 123277 | 7.1% |
| t | 106130 | 6.1% |
| o | 91692 | 5.3% |
| , | 77281 | 4.4% |
| r | 76086 | 4.4% |
| s | 60614 | 3.5% |
| Other values (165) | 605459 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 1740002 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 207445 | 11.9% | |
| e | 139230 | 8.0% |
| n | 127950 | 7.4% |
| i | 124838 | 7.2% |
| a | 123277 | 7.1% |
| t | 106130 | 6.1% |
| o | 91692 | 5.3% |
| , | 77281 | 4.4% |
| r | 76086 | 4.4% |
| s | 60614 | 3.5% |
| Other values (165) | 605459 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 1740002 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 207445 | 11.9% | |
| e | 139230 | 8.0% |
| n | 127950 | 7.4% |
| i | 124838 | 7.2% |
| a | 123277 | 7.1% |
| t | 106130 | 6.1% |
| o | 91692 | 5.3% |
| , | 77281 | 4.4% |
| r | 76086 | 4.4% |
| s | 60614 | 3.5% |
| Other values (165) | 605459 |
Authors with affiliations
Text
Missing
| Distinct | 11330 |
|---|---|
| Distinct (%) | 96.0% |
| Missing | 384 |
| Missing (%) | 3.1% |
| Memory size | 5.7 MiB |
Length
| Max length | 4955 |
|---|---|
| Median length | 943.5 |
| Mean length | 317.91421 |
| Min length | 10 |
Unique
| Unique | 10960 ? |
|---|---|
| Unique (%) | 92.8% |
Sample
| 1st row | Wang, Yang, Nanjing Normal University, Nanjing, Jiangsu, China |
|---|---|
| 2nd row | Lin, Yuru, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, China; Zhang, Yi, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, China; Yang, Yuqin, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, China; Pan, Shidan, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, China; Ren, Xu, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, China; Chen, Dengkang, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, China |
| 3rd row | Hsu, Tingchia, Department of Technology Application and Human Resource Development, National Taiwan Normal University, Taipei, Taiwan; Hsu, Taiping, Department of Technology Application and Human Resource Development, National Taiwan Normal University, Taipei, Taiwan |
| 4th row | Aksoy, Behiye Dinçer, General Directorate of Innovation and Educational Technologies, Ankara, Turkey; Mumcu, Filiz Kuşkaya, Department of Early Childhood Education, Universität Graz, Graz, Styria, Austria; Cantürk Günhan, Berna, Department of Mathematics and Science Education, Dokuz Eylül Üniversitesi, Izmir, Turkey |
| 5th row | van Bergen, Ruben S., Radboud Universiteit, Nijmegen, Gelderland, Netherlands; Huebotter, Justus F., Radboud Universiteit, Nijmegen, Gelderland, Netherlands; null, null, Cajal International Center for Neuroscience, Consejo Superior de Investigaciones Científicas, Madrid, Madrid, Spain; Lanillos, Pablo, Radboud Universiteit, Nijmegen, Gelderland, Netherlands, Cajal International Center for Neuroscience, Consejo Superior de Investigaciones Científicas, Madrid, Madrid, Spain |
| Value | Count | Frequency (%) |
| of | 28388 | 6.0% |
| university | 19900 | 4.2% |
| united | 13487 | 2.8% |
| states | 11963 | 2.5% |
| department | 9040 | 1.9% |
| and | 8643 | 1.8% |
| china | 5560 | 1.2% |
| de | 5551 | 1.2% |
| education | 4712 | 1.0% |
| science | 4637 | 1.0% |
| Other values (36362) | 363675 |
Most occurring characters
| Value | Count | Frequency (%) |
| 463999 | 12.4% | |
| e | 283764 | 7.6% |
| a | 279890 | 7.5% |
| n | 268021 | 7.1% |
| i | 263180 | 7.0% |
| , | 214281 | 5.7% |
| t | 198701 | 5.3% |
| o | 188030 | 5.0% |
| r | 162102 | 4.3% |
| s | 123421 | 3.3% |
| Other values (209) | 1308542 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 3753931 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 463999 | 12.4% | |
| e | 283764 | 7.6% |
| a | 279890 | 7.5% |
| n | 268021 | 7.1% |
| i | 263180 | 7.0% |
| , | 214281 | 5.7% |
| t | 198701 | 5.3% |
| o | 188030 | 5.0% |
| r | 162102 | 4.3% |
| s | 123421 | 3.3% |
| Other values (209) | 1308542 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 3753931 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 463999 | 12.4% | |
| e | 283764 | 7.6% |
| a | 279890 | 7.5% |
| n | 268021 | 7.1% |
| i | 263180 | 7.0% |
| , | 214281 | 5.7% |
| t | 198701 | 5.3% |
| o | 188030 | 5.0% |
| r | 162102 | 4.3% |
| s | 123421 | 3.3% |
| Other values (209) | 1308542 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 3753931 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 463999 | 12.4% | |
| e | 283764 | 7.6% |
| a | 279890 | 7.5% |
| n | 268021 | 7.1% |
| i | 263180 | 7.0% |
| , | 214281 | 5.7% |
| t | 198701 | 5.3% |
| o | 188030 | 5.0% |
| r | 162102 | 4.3% |
| s | 123421 | 3.3% |
| Other values (209) | 1308542 |
Abstract
Text
| Distinct | 11972 |
|---|---|
| Distinct (%) | 98.2% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 35.8 MiB |
Length
| Max length | 13301 |
|---|---|
| Median length | 2186.5 |
| Mean length | 1309.4096 |
| Min length | 23 |
Unique
| Unique | 11934 ? |
|---|---|
| Unique (%) | 97.9% |
Sample
| 1st row | Learning engagement is an important indicator of active learning outcomes. Computational thinking is a basic competency required in the 21st century. Troubleshooting learning is helpful to enhance students’ computational thinking and engagement, as its targeted error analysis addresses traditional learning’s limitation of insufficient guidance on error-prone points. However, the role of troubleshooting in students’ engagement and computational thinking in robotics programming learning is to be explored. To fill in this gap, the current study explored the effects of troubleshooting robotics programming learning on students’ engagement, computational thinking, and programming skills. A quasi-experimental study was conducted to explore the effects of troubleshooting learning on students’ robotics programming learning by comparing students’ learning results in two courses instructed by the same instructor (one instructed with a problem-based method, the other instructed with a troubleshooting method). The participants were seventy-nine students from a university in China. Questionnaires, tests, and work analyses were used to measure students’ engagement, computational thinking, and programming skills. The results indicated that troubleshooting learning is more effective in enhancing students’ engagement (i.e., behavioral, cognitive, and emotional engagement), computational thinking (i.e., cooperativity, critical thinking, and creativity) and programming learning (i.e., data representation). The findings provide insight into troubleshooting-supported robotics programming learning. Different types of troubleshooting tasks with progressive difficulty are effective in enhancing students’ learning. Troubleshooting could be used in the early stages of programming learning to help students master the error prone areas of programming. © 2025 Elsevier Ltd. |
|---|---|
| 2nd row | The integration of artificial intelligence (AI) tools in education to promote computational thinking (CT) among students has become a trending topic of research; however, there is no consensus on the impact of such tools on CT. Qualitative syntheses regarding both the effect of AI tools and how to unleash their power more effectively are also lacking. Using a three-level meta-analytic approach, this study evaluated the effectiveness of AI tools in improving students’ CT and investigated the various moderating variables. A total of 32 empirical studies with 44 effect sizes were included in this meta-analysis, and the results showed that AI tools have a significant and moderately large effect on students’ CT (Hedges’s g = 0.75, 95 % CI [0.55, 0.95], p < 0.0001). Moderator analyses revealed that AI technologies, the application of AI tools, as well as tool customization and its method, and sample size significantly influence the effectiveness of AI tools. Other moderators—including region, publication year, subject disciplines, instructional approach, collaboration type, intervention duration, gender, and educational level—appeared to be universally effective in promoting student CT. Overall, this meta-analysis contributes to both the academic understanding and practical application of AI tools in CT education to help students prepare for the smart society of the future. © 2025 Elsevier Ltd. |
| 3rd row | The study developed an online game system for young students to learn computational thinking (CT), and explored the CT learning achievements and self-efficacy of students using two thinking-guided methods. One method was 5W1H, which is well known in science learning, and the other was concept-association-based concept mapping (CABCM). These thinking-guided methods, aimed at the beginning stage of problem analysis, were utilized before playing the online game, with the aim of helping students learn and solve CT tasks in the game scenarios. The research involved 54 students whose average age was 10, divided into two groups based on the different thinking-guided methods. The experimental results showed that students in both the CABCM and 5W1H groups demonstrated significant learning gains in CT achievement and self-efficacy from pre-test to post-test. While no statistically significant difference was found in the post-test scores between the two groups, a detailed analysis of learning behaviors revealed distinct problem-solving pathways associated with each thinking-guided method. The findings suggest that both integrated approaches effectively fostered CT skills, albeit through different cognitive processes. This research contributes to CT education by integrating thinking-guided methods into an online CT game. It offers empirical evidence on the effectiveness of such integrated approaches and provides insights into the processes and behaviors associated with different thinking-guided methods, shedding light on students' challenges in learning CT through games. © © 2025. Published by Elsevier Ltd. |
| 4th row | This study explores how Computational Thinking (CT) components overlap with the phases of mathematical modelling within the context of a Teacher Development Course (TDC). The course was designed, developed, implemented, and assessed to enhance teachers’ cognitive actions in integrating CT with mathematical modelling. This research study was conducted with three mathematics teachers and one computer science teacher. Data were collected through CT component worksheets and video recordings, and analysed based on Borromeo-Ferri’s (2006) modelling cycle and the study’s CT framework. The study’s findings indicate that modelling processes enhanced teachers’ CT skills, while CT components made the modelling process more structured and reflective, revealing a reciprocal relationship between modelling and CT. The study proposes an original interdisciplinary framework linking teachers’ cognitive actions to CT integration, offering both theoretical and practical contributions. © 2025 The Author(s). |
| 5th row | Autonomous intelligent agents must bridge computational challenges at disparate levels of abstraction, from the low-level spaces of sensory input and motor commands to the high-level domain of abstract reasoning and planning. A key question in designing such agents is how best to instantiate the representational space that will interface between these two levels—ideally without requiring supervision in the form of expensive data annotations. These objectives can be efficiently achieved by representing the world in terms of objects (grounded in perception and action). In this work, we present a novel, brain-inspired, deep-learning architecture that learns from pixels to interpret, control, and reason about its environment, using object-centric representations. We show the utility of our approach through tasks in synthetic environments that require a combination of (high-level) logical reasoning and (low-level) continuous control. Results show that the agent can learn emergent conditional behavioural reasoning, such as (A → B)∧(¬A → C), as well as logical composition (A → B)∧(A → C)⊢A → (B∧C) and XOR operations, and successfully controls its environment to satisfy objectives deduced from these logical rules. The agent can adapt online to unexpected changes in its environment and is robust to mild violations of its world model, thanks to dynamic internal desired goal generation. While the present results are limited to synthetic settings (2D and 3D activated versions of dSprites), which fall short of real-world levels of complexity, the proposed architecture shows how to manipulate grounded object representations, as a key inductive bias for unsupervised learning, to enable behavioral reasoning. © 2025 The Author(s) |
| Value | Count | Frequency (%) |
| the | 128221 | 5.6% |
| and | 88557 | 3.9% |
| of | 87066 | 3.8% |
| to | 58800 | 2.6% |
| in | 57532 | 2.5% |
| a | 43907 | 1.9% |
| for | 25277 | 1.1% |
| that | 22114 | 1.0% |
| is | 21623 | 0.9% |
| this | 21381 | 0.9% |
| Other values (57066) | 1729718 |
Most occurring characters
| Value | Count | Frequency (%) |
| 2270696 | ||
| e | 1519726 | 9.5% |
| t | 1164801 | 7.3% |
| i | 1105917 | 6.9% |
| n | 1045031 | 6.5% |
| a | 1013964 | 6.4% |
| o | 947035 | 5.9% |
| s | 872806 | 5.5% |
| r | 778827 | 4.9% |
| c | 551279 | 3.5% |
| Other values (422) | 4694240 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 15964322 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 2270696 | ||
| e | 1519726 | 9.5% |
| t | 1164801 | 7.3% |
| i | 1105917 | 6.9% |
| n | 1045031 | 6.5% |
| a | 1013964 | 6.4% |
| o | 947035 | 5.9% |
| s | 872806 | 5.5% |
| r | 778827 | 4.9% |
| c | 551279 | 3.5% |
| Other values (422) | 4694240 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 15964322 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 2270696 | ||
| e | 1519726 | 9.5% |
| t | 1164801 | 7.3% |
| i | 1105917 | 6.9% |
| n | 1045031 | 6.5% |
| a | 1013964 | 6.4% |
| o | 947035 | 5.9% |
| s | 872806 | 5.5% |
| r | 778827 | 4.9% |
| c | 551279 | 3.5% |
| Other values (422) | 4694240 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 15964322 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 2270696 | ||
| e | 1519726 | 9.5% |
| t | 1164801 | 7.3% |
| i | 1105917 | 6.9% |
| n | 1045031 | 6.5% |
| a | 1013964 | 6.4% |
| o | 947035 | 5.9% |
| s | 872806 | 5.5% |
| r | 778827 | 4.9% |
| c | 551279 | 3.5% |
| Other values (422) | 4694240 |
Author Keywords
Text
Missing
| Distinct | 9188 |
|---|---|
| Distinct (%) | 99.1% |
| Missing | 2918 |
| Missing (%) | 23.9% |
| Memory size | 1.4 MiB |
Length
| Max length | 930 |
|---|---|
| Median length | 278 |
| Mean length | 97.580116 |
| Min length | 6 |
Unique
| Unique | 9106 ? |
|---|---|
| Unique (%) | 98.2% |
Sample
| 1st row | Computational thinking; Programming skills; Robotics programming learning; Troubleshooting |
|---|---|
| 2nd row | Artificial intelligence; Artificial intelligence in education; Computational thinking; Moderator analysis; Three-level meta-analysis |
| 3rd row | 5W1H; Computational thinking; Concept-association-based concept mapping strategy; Self-efficacy |
| 4th row | Computational thinking; CT components; CT-integrated maths education; Mathematical modelling; Teacher development |
| 5th row | Brain-inspired perception and control; Deep learning architectures; Object-centric reasoning |
| Value | Count | Frequency (%) |
| computational | 5920 | 6.4% |
| thinking | 5876 | 6.3% |
| education | 3133 | 3.4% |
| learning | 2605 | 2.8% |
| programming | 1708 | 1.8% |
| science | 1181 | 1.3% |
| design | 1127 | 1.2% |
| computer | 939 | 1.0% |
| and | 795 | 0.9% |
| of | 684 | 0.7% |
| Other values (9696) | 69032 |
Most occurring characters
| Value | Count | Frequency (%) |
| 83716 | 9.3% | |
| i | 79123 | 8.7% |
| n | 70955 | 7.8% |
| e | 66177 | 7.3% |
| t | 64142 | 7.1% |
| a | 62228 | 6.9% |
| o | 56863 | 6.3% |
| r | 39633 | 4.4% |
| ; | 37871 | 4.2% |
| l | 36114 | 4.0% |
| Other values (131) | 308136 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 904958 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 83716 | 9.3% | |
| i | 79123 | 8.7% |
| n | 70955 | 7.8% |
| e | 66177 | 7.3% |
| t | 64142 | 7.1% |
| a | 62228 | 6.9% |
| o | 56863 | 6.3% |
| r | 39633 | 4.4% |
| ; | 37871 | 4.2% |
| l | 36114 | 4.0% |
| Other values (131) | 308136 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 904958 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 83716 | 9.3% | |
| i | 79123 | 8.7% |
| n | 70955 | 7.8% |
| e | 66177 | 7.3% |
| t | 64142 | 7.1% |
| a | 62228 | 6.9% |
| o | 56863 | 6.3% |
| r | 39633 | 4.4% |
| ; | 37871 | 4.2% |
| l | 36114 | 4.0% |
| Other values (131) | 308136 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 904958 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 83716 | 9.3% | |
| i | 79123 | 8.7% |
| n | 70955 | 7.8% |
| e | 66177 | 7.3% |
| t | 64142 | 7.1% |
| a | 62228 | 6.9% |
| o | 56863 | 6.3% |
| r | 39633 | 4.4% |
| ; | 37871 | 4.2% |
| l | 36114 | 4.0% |
| Other values (131) | 308136 |
Index Keywords
Text
Missing
| Distinct | 7351 |
|---|---|
| Distinct (%) | 99.8% |
| Missing | 4826 |
| Missing (%) | 39.6% |
| Memory size | 2.5 MiB |
Length
| Max length | 2655 |
|---|---|
| Median length | 692 |
| Mean length | 275.7107 |
| Min length | 7 |
Unique
| Unique | 7336 ? |
|---|---|
| Unique (%) | 99.6% |
Sample
| 1st row | Abstracting; Architecture; Behavioral research; Deep learning; Intelligent agents; Memory architecture; Unsupervised learning; Autonomous Intelligent Agents; Behavioral reasoning; Brain-inspired; Brain-inspired perception and control; Computational challenges; Deep learning architecture; Learn+; Learning architectures; Object-centric reasoning; Sensory motors; Autonomous agents; abstract thinking; article; clinical article; controlled study; deep learning; human; learning; logical reasoning; reasoning; sensory stimulation |
|---|---|
| 2nd row | Computational methods; E-learning; Teaching; Academic achievements; Computational thinkings; Computer literacy; Digital competency; Digital skills; ICT use; Information literacy; Mediation effect; Multi-group; Self efficacy; Students |
| 3rd row | Brain; Cognitive systems; Dynamics; Neural networks; Neurons; Stability; Meta-stable state; Multiple neural timescale; Neural activity; Sequential patterns; Speed modulation; Task difficulty; Temporal modulations; Temporal scaling; Time-scales; Working memory; Computation theory; article; artificial neural network; cognition; controlled study; dwell time; human experiment; learning; mental performance; nerve cell network; nonhuman; speech; thinking; velocity; working memory |
| 4th row | Computational methods; Machine learning; Current modeling; Explainability; Learn+; Phase 1; Pre-training; Radiology report generation; Radiology reports; Reinforcement learnings; Report generation; Training framework; Radiology; article; benchmarking; human; large language model; radiologist; reasoning; thinking; thorax radiography; X ray; X ray analysis |
| 5th row | Artificial intelligence; Fuel additives; Input output programs; Iterative methods; Learning systems; Systems analysis; Systems thinking; Test facilities; Active Learning; Adaptive sampling; Cluster-based; Computational effort; Machine-learning; Real-world; Sampling technique; Surrogate modeling; System models; Test-functions; Design of experiments |
| Value | Count | Frequency (%) |
| computational | 6807 | 3.3% |
| education | 4929 | 2.4% |
| learning | 4707 | 2.3% |
| computer | 3876 | 1.9% |
| thinkings | 3669 | 1.8% |
| programming | 3221 | 1.5% |
| students | 3139 | 1.5% |
| systems | 2864 | 1.4% |
| engineering | 2514 | 1.2% |
| thinking | 2194 | 1.1% |
| Other values (11786) | 170780 |
Most occurring characters
| Value | Count | Frequency (%) |
| 201334 | 9.9% | |
| e | 162044 | 8.0% |
| i | 158684 | 7.8% |
| n | 150294 | 7.4% |
| t | 136501 | 6.7% |
| a | 131589 | 6.5% |
| o | 124227 | 6.1% |
| ; | 102182 | 5.0% |
| s | 99565 | 4.9% |
| r | 98152 | 4.8% |
| Other values (109) | 666313 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 2030885 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 201334 | 9.9% | |
| e | 162044 | 8.0% |
| i | 158684 | 7.8% |
| n | 150294 | 7.4% |
| t | 136501 | 6.7% |
| a | 131589 | 6.5% |
| o | 124227 | 6.1% |
| ; | 102182 | 5.0% |
| s | 99565 | 4.9% |
| r | 98152 | 4.8% |
| Other values (109) | 666313 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 2030885 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 201334 | 9.9% | |
| e | 162044 | 8.0% |
| i | 158684 | 7.8% |
| n | 150294 | 7.4% |
| t | 136501 | 6.7% |
| a | 131589 | 6.5% |
| o | 124227 | 6.1% |
| ; | 102182 | 5.0% |
| s | 99565 | 4.9% |
| r | 98152 | 4.8% |
| Other values (109) | 666313 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 2030885 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 201334 | 9.9% | |
| e | 162044 | 8.0% |
| i | 158684 | 7.8% |
| n | 150294 | 7.4% |
| t | 136501 | 6.7% |
| a | 131589 | 6.5% |
| o | 124227 | 6.1% |
| ; | 102182 | 5.0% |
| s | 99565 | 4.9% |
| r | 98152 | 4.8% |
| Other values (109) | 666313 |
Molecular Sequence Numbers
Text
Missing
| Distinct | 4 |
|---|---|
| Distinct (%) | 100.0% |
| Missing | 12188 |
| Missing (%) | > 99.9% |
| Memory size | 381.6 KiB |
Length
| Max length | 223 |
|---|---|
| Median length | 98 |
| Mean length | 116 |
| Min length | 45 |
Unique
| Unique | 4 ? |
|---|---|
| Unique (%) | 100.0% |
Sample
| 1st row | GENBANK: KP311695:KP311894, KP311895:KP311944 |
|---|---|
| 2nd row | SWISSPROT: P29466, P31944, P42575, P49662, P51878, P55210, P55211, P55212, Q14790, Q92851 |
| 3rd row | PIR: O60675, P05164, P13693, P17480, P30626, P31948, P33241, P35398, P51858, Q13951, Q14103, Q14393, Q92945 |
| 4th row | GENBANK: RS105147, RS165599, RS165656, RS165688, RS174682, RS174694, RS284786, RS284787, RS475325, RS581105, RS598156, RS6263, RS6323, RS729147, RS740603, RS887241, RS894369, RS917520, RS921450, RS933269, RS938328, RS971074 |
| Value | Count | Frequency (%) |
| genbank | 2 | 3.9% |
| kp311695:kp311894 | 1 | 2.0% |
| kp311895:kp311944 | 1 | 2.0% |
| swissprot | 1 | 2.0% |
| p29466 | 1 | 2.0% |
| p31944 | 1 | 2.0% |
| p42575 | 1 | 2.0% |
| p49662 | 1 | 2.0% |
| p51878 | 1 | 2.0% |
| p55210 | 1 | 2.0% |
| Other values (40) | 40 |
Most occurring characters
| Value | Count | Frequency (%) |
| 47 | 10.1% | |
| , | 43 | 9.3% |
| 1 | 42 | 9.1% |
| 5 | 33 | 7.1% |
| 4 | 29 | 6.2% |
| 9 | 29 | 6.2% |
| 6 | 27 | 5.8% |
| 3 | 27 | 5.8% |
| S | 25 | 5.4% |
| R | 24 | 5.2% |
| Other values (17) | 138 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 464 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 47 | 10.1% | |
| , | 43 | 9.3% |
| 1 | 42 | 9.1% |
| 5 | 33 | 7.1% |
| 4 | 29 | 6.2% |
| 9 | 29 | 6.2% |
| 6 | 27 | 5.8% |
| 3 | 27 | 5.8% |
| S | 25 | 5.4% |
| R | 24 | 5.2% |
| Other values (17) | 138 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 464 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 47 | 10.1% | |
| , | 43 | 9.3% |
| 1 | 42 | 9.1% |
| 5 | 33 | 7.1% |
| 4 | 29 | 6.2% |
| 9 | 29 | 6.2% |
| 6 | 27 | 5.8% |
| 3 | 27 | 5.8% |
| S | 25 | 5.4% |
| R | 24 | 5.2% |
| Other values (17) | 138 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 464 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 47 | 10.1% | |
| , | 43 | 9.3% |
| 1 | 42 | 9.1% |
| 5 | 33 | 7.1% |
| 4 | 29 | 6.2% |
| 9 | 29 | 6.2% |
| 6 | 27 | 5.8% |
| 3 | 27 | 5.8% |
| S | 25 | 5.4% |
| R | 24 | 5.2% |
| Other values (17) | 138 |
Chemicals/CAS
Text
Missing
| Distinct | 123 |
|---|---|
| Distinct (%) | 92.5% |
| Missing | 12059 |
| Missing (%) | 98.9% |
| Memory size | 395.8 KiB |
Length
| Max length | 872 |
|---|---|
| Median length | 147 |
| Mean length | 95.842105 |
| Min length | 5 |
Unique
| Unique | 117 ? |
|---|---|
| Unique (%) | 88.0% |
Sample
| 1st row | alanine aminotransferase, 9000-86-6, 9014-30-6 |
|---|---|
| 2nd row | ketamine, 1867-66-9, 6740-88-1, 81771-21-3; temozolomide, 85622-93-1; transforming growth factor beta receptor 1; transforming growth factor beta receptor 2; xylazine, 23076-35-9, 7361-61-7; Smad protein, 62395-38-4; MicroRNAs; MIRN590 microRNA, human; Octamer Transcription Factor-3; poly(beta-amino ester); Polymers; POU5F1 protein, human; Receptor, Transforming Growth Factor-beta Type II; Smad Proteins; SOX2 protein, human; SOXB1 Transcription Factors; Temozolomide; TGFBR2 protein, human |
| 3rd row | amino acid, 65072-01-7; Chromatin |
| 4th row | carbon dioxide, 124-38-9, 58561-67-4 |
| 5th row | carbon, 7440-44-0; nitrophenol, 25154-55-6; Carbon; Nitrophenols; Water Pollutants, Chemical |
| Value | Count | Frequency (%) |
| protein | 47 | 3.5% |
| 0 | 38 | 2.8% |
| proteins | 25 | 1.8% |
| rna | 24 | 1.8% |
| dna | 21 | 1.5% |
| dopamine | 17 | 1.3% |
| human | 16 | 1.2% |
| acid | 15 | 1.1% |
| 51-61-6 | 14 | 1.0% |
| kinase | 13 | 1.0% |
| Other values (762) | 1126 |
Most occurring characters
| Value | Count | Frequency (%) |
| 1223 | 9.6% | |
| - | 785 | 6.2% |
| e | 728 | 5.7% |
| i | 612 | 4.8% |
| n | 560 | 4.4% |
| a | 557 | 4.4% |
| o | 549 | 4.3% |
| r | 475 | 3.7% |
| , | 461 | 3.6% |
| t | 461 | 3.6% |
| Other values (58) | 6336 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 12747 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 1223 | 9.6% | |
| - | 785 | 6.2% |
| e | 728 | 5.7% |
| i | 612 | 4.8% |
| n | 560 | 4.4% |
| a | 557 | 4.4% |
| o | 549 | 4.3% |
| r | 475 | 3.7% |
| , | 461 | 3.6% |
| t | 461 | 3.6% |
| Other values (58) | 6336 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 12747 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 1223 | 9.6% | |
| - | 785 | 6.2% |
| e | 728 | 5.7% |
| i | 612 | 4.8% |
| n | 560 | 4.4% |
| a | 557 | 4.4% |
| o | 549 | 4.3% |
| r | 475 | 3.7% |
| , | 461 | 3.6% |
| t | 461 | 3.6% |
| Other values (58) | 6336 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 12747 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 1223 | 9.6% | |
| - | 785 | 6.2% |
| e | 728 | 5.7% |
| i | 612 | 4.8% |
| n | 560 | 4.4% |
| a | 557 | 4.4% |
| o | 549 | 4.3% |
| r | 475 | 3.7% |
| , | 461 | 3.6% |
| t | 461 | 3.6% |
| Other values (58) | 6336 |
Tradenames
Text
Missing
| Distinct | 18 |
|---|---|
| Distinct (%) | 100.0% |
| Missing | 12174 |
| Missing (%) | 99.9% |
| Memory size | 382.1 KiB |
Length
| Max length | 179 |
|---|---|
| Median length | 26.5 |
| Mean length | 35.722222 |
| Min length | 5 |
Unique
| Unique | 18 ? |
|---|---|
| Unique (%) | 100.0% |
Sample
| 1st row | Attune NxT flow cytometer, Thermo; MAGnify Chromatin Immunoprecipitation System, Life Technologies; Odyssey Infrared Imager, LI COR; SCpubr R package v1.1.2; Seurat R package v4.4 |
|---|---|
| 2nd row | MAXQDA software Version 2022; Smart-PLS software Version 4.0 |
| 3rd row | MATLAB; Psychtoolbox-3; rstan package in R; Siemens Prisma MRI scanner, Siemens |
| 4th row | Cytoscape |
| 5th row | HydroCel, Electrical Geodesics |
| Value | Count | Frequency (%) |
| siemens | 8 | 9.1% |
| r | 3 | 3.4% |
| package | 3 | 3.4% |
| system | 2 | 2.3% |
| software | 2 | 2.3% |
| tim | 2 | 2.3% |
| trio | 2 | 2.3% |
| alere | 2 | 2.3% |
| scanner | 2 | 2.3% |
| version | 2 | 2.3% |
| Other values (59) | 60 |
Most occurring characters
| Value | Count | Frequency (%) |
| 70 | 10.9% | |
| e | 62 | 9.6% |
| i | 35 | 5.4% |
| a | 35 | 5.4% |
| n | 34 | 5.3% |
| r | 32 | 5.0% |
| s | 31 | 4.8% |
| o | 28 | 4.4% |
| m | 23 | 3.6% |
| t | 23 | 3.6% |
| Other values (46) | 270 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 643 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 70 | 10.9% | |
| e | 62 | 9.6% |
| i | 35 | 5.4% |
| a | 35 | 5.4% |
| n | 34 | 5.3% |
| r | 32 | 5.0% |
| s | 31 | 4.8% |
| o | 28 | 4.4% |
| m | 23 | 3.6% |
| t | 23 | 3.6% |
| Other values (46) | 270 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 643 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 70 | 10.9% | |
| e | 62 | 9.6% |
| i | 35 | 5.4% |
| a | 35 | 5.4% |
| n | 34 | 5.3% |
| r | 32 | 5.0% |
| s | 31 | 4.8% |
| o | 28 | 4.4% |
| m | 23 | 3.6% |
| t | 23 | 3.6% |
| Other values (46) | 270 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 643 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 70 | 10.9% | |
| e | 62 | 9.6% |
| i | 35 | 5.4% |
| a | 35 | 5.4% |
| n | 34 | 5.3% |
| r | 32 | 5.0% |
| s | 31 | 4.8% |
| o | 28 | 4.4% |
| m | 23 | 3.6% |
| t | 23 | 3.6% |
| Other values (46) | 270 |
Manufacturers
Text
Missing
| Distinct | 6 |
|---|---|
| Distinct (%) | 66.7% |
| Missing | 12183 |
| Missing (%) | 99.9% |
| Memory size | 381.4 KiB |
Length
| Max length | 33 |
|---|---|
| Median length | 20 |
| Mean length | 11.777778 |
| Min length | 5 |
Unique
| Unique | 5 ? |
|---|---|
| Unique (%) | 55.6% |
Sample
| 1st row | Thermo; Life Technologies; LI COR |
|---|---|
| 2nd row | Siemens |
| 3rd row | Electrical Geodesics |
| 4th row | Apple |
| 5th row | Alere; Siemens |
| Value | Count | Frequency (%) |
| siemens | 5 | |
| thermo | 1 | 6.7% |
| life | 1 | 6.7% |
| technologies | 1 | 6.7% |
| li | 1 | 6.7% |
| cor | 1 | 6.7% |
| electrical | 1 | 6.7% |
| geodesics | 1 | 6.7% |
| apple | 1 | 6.7% |
| alere | 1 | 6.7% |
Most occurring characters
| Value | Count | Frequency (%) |
| e | 20 | |
| i | 10 | 9.4% |
| s | 8 | 7.5% |
| n | 7 | 6.6% |
| m | 6 | 5.7% |
| 6 | 5.7% | |
| S | 5 | 4.7% |
| l | 5 | 4.7% |
| o | 4 | 3.8% |
| c | 4 | 3.8% |
| Other values (19) | 31 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 106 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| e | 20 | |
| i | 10 | 9.4% |
| s | 8 | 7.5% |
| n | 7 | 6.6% |
| m | 6 | 5.7% |
| 6 | 5.7% | |
| S | 5 | 4.7% |
| l | 5 | 4.7% |
| o | 4 | 3.8% |
| c | 4 | 3.8% |
| Other values (19) | 31 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 106 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| e | 20 | |
| i | 10 | 9.4% |
| s | 8 | 7.5% |
| n | 7 | 6.6% |
| m | 6 | 5.7% |
| 6 | 5.7% | |
| S | 5 | 4.7% |
| l | 5 | 4.7% |
| o | 4 | 3.8% |
| c | 4 | 3.8% |
| Other values (19) | 31 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 106 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| e | 20 | |
| i | 10 | 9.4% |
| s | 8 | 7.5% |
| n | 7 | 6.6% |
| m | 6 | 5.7% |
| 6 | 5.7% | |
| S | 5 | 4.7% |
| l | 5 | 4.7% |
| o | 4 | 3.8% |
| c | 4 | 3.8% |
| Other values (19) | 31 |
Funding Details
Text
Missing
| Distinct | 3412 |
|---|---|
| Distinct (%) | 87.8% |
| Missing | 8305 |
| Missing (%) | 68.1% |
| Memory size | 1019.0 KiB |
Length
| Max length | 3365 |
|---|---|
| Median length | 435 |
| Mean length | 110.01415 |
| Min length | 5 |
Unique
| Unique | 3144 ? |
|---|---|
| Unique (%) | 80.9% |
Sample
| 1st row | Ministry of Education, MOE; Major Project of Philosophy and Social Science Research in Colleges and Universities of Jiangsu Province, (25JYC004) |
|---|---|
| 2nd row | National Natural Science Foundation of China, NSFC, (72274076); Fundamental Research Funds for the Central Universities, (30106250032) |
| 3rd row | (NSTC 111-2410-H-003-168-MY3) |
| 4th row | National Taiwan Normal University, NTNU; International Association for the Evaluation of Educational Achievement, IEA; National Science and Technology Council, NSTC; Ministry of Education, MOE |
| 5th row | Economic and Social Research Council, SSRC, (2267832) |
| Value | Count | Frequency (%) |
| science | 2802 | 5.5% |
| of | 2772 | 5.4% |
| national | 2683 | 5.3% |
| foundation | 2435 | 4.8% |
| nsf | 1486 | 2.9% |
| and | 1232 | 2.4% |
| research | 1062 | 2.1% |
| de | 869 | 1.7% |
| university | 758 | 1.5% |
| china | 745 | 1.5% |
| Other values (7838) | 34045 |
Most occurring characters
| Value | Count | Frequency (%) |
| 47001 | 11.0% | |
| n | 27796 | 6.5% |
| a | 24581 | 5.7% |
| i | 24013 | 5.6% |
| e | 23758 | 5.6% |
| o | 22604 | 5.3% |
| t | 15582 | 3.6% |
| c | 13351 | 3.1% |
| , | 12837 | 3.0% |
| 0 | 11568 | 2.7% |
| Other values (162) | 204534 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 427625 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 47001 | 11.0% | |
| n | 27796 | 6.5% |
| a | 24581 | 5.7% |
| i | 24013 | 5.6% |
| e | 23758 | 5.6% |
| o | 22604 | 5.3% |
| t | 15582 | 3.6% |
| c | 13351 | 3.1% |
| , | 12837 | 3.0% |
| 0 | 11568 | 2.7% |
| Other values (162) | 204534 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 427625 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 47001 | 11.0% | |
| n | 27796 | 6.5% |
| a | 24581 | 5.7% |
| i | 24013 | 5.6% |
| e | 23758 | 5.6% |
| o | 22604 | 5.3% |
| t | 15582 | 3.6% |
| c | 13351 | 3.1% |
| , | 12837 | 3.0% |
| 0 | 11568 | 2.7% |
| Other values (162) | 204534 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 427625 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 47001 | 11.0% | |
| n | 27796 | 6.5% |
| a | 24581 | 5.7% |
| i | 24013 | 5.6% |
| e | 23758 | 5.6% |
| o | 22604 | 5.3% |
| t | 15582 | 3.6% |
| c | 13351 | 3.1% |
| , | 12837 | 3.0% |
| 0 | 11568 | 2.7% |
| Other values (162) | 204534 |
Funding Texts
Text
Missing
| Distinct | 4024 |
|---|---|
| Distinct (%) | 97.0% |
| Missing | 8043 |
| Missing (%) | 66.0% |
| Memory size | 3.4 MiB |
Length
| Max length | 92320 |
|---|---|
| Median length | 1268 |
| Mean length | 458.8872 |
| Min length | 4 |
Unique
| Unique | 3938 ? |
|---|---|
| Unique (%) | 94.9% |
Sample
| 1st row | This work was supported by the Project of Humanities and Social Sciences Program of the Ministry of Education , the Philosophy and Social Science Research project of Jiangsu province (No. 25JYC004 ). |
|---|---|
| 2nd row | This study was funded by the 2023 National Natural Science Foundation of China (Grant No. 72274076) and funded by the Fundamental Research Funds for the Central Universities (Outstanding Innovation Project, No. 30106250032). |
| 3rd row | This study is supported in part by the National Science and Technology Council in the Republic of China under contract numbers NSTC 111-2410-H-003-168-MY3 . |
| 4th row | The authors thank to the financial support from The National Science and Technology Council, Taiwan, and The Ministry of Education, Taiwan. Special thanks go to the colleagues from the IEA in Hamberg, Germany, the IEA in Amsterdam, Neitherlands, and the ICILS 2023 National Research Center at NTNU, Taiwan for their excellent research collaboration in the ICILS 2023 study. |
| 5th row | Funding text 1: This research was funded by the Economic and Social Research Council, granted to Sarah A. Gerson and Johanna E. van Schaik, with in-kind contributions from Primo Toys/Moravia Education and Techniquest (reference 2267832).We are grateful to the schools, teachers, and children who participated in this research. We also thank the BSc and MSc students \u2013 Lloyd, Alexandra, Chara, Georgie, Zoe, Matt, Ellie, and Jamie \u2013 for their support with data processing. Special thanks to Dr Dominic Guitard and Dr Kelsey Frewin for their statistical advice, and to Vicky Simmons for her invaluable help in collecting the large volume of data. We would also like to extend our gratitude to the reviewers for providing fruitful feedback. We appreciate the support of Primo Toys/Moravia Consulting for providing essential resources. This project was funded by the Economic and Social Research Council through the Doctoral Training Partnership.; Funding text 2: This research was funded by the Economic and Social Research Council , granted to Sarah A. Gerson and Johanna E. van Schaik, with in-kind contributions from Primo Toys/Moravia Education and Techniquest (reference 2267832 ). |
| Value | Count | Frequency (%) |
| the | 18688 | 6.8% |
| and | 11291 | 4.1% |
| of | 10245 | 3.7% |
| by | 5377 | 1.9% |
| this | 5231 | 1.9% |
| in | 4634 | 1.7% |
| for | 4530 | 1.6% |
| to | 4477 | 1.6% |
| research | 4450 | 1.6% |
| supported | 2943 | 1.1% |
| Other values (26623) | 204834 |
Most occurring characters
| Value | Count | Frequency (%) |
| 272527 | 14.3% | |
| e | 152683 | 8.0% |
| n | 117118 | 6.2% |
| t | 114955 | 6.0% |
| a | 113835 | 6.0% |
| o | 108417 | 5.7% |
| i | 105775 | 5.6% |
| r | 95378 | 5.0% |
| s | 80596 | 4.2% |
| h | 58171 | 3.1% |
| Other values (343) | 684468 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 1903923 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 272527 | 14.3% | |
| e | 152683 | 8.0% |
| n | 117118 | 6.2% |
| t | 114955 | 6.0% |
| a | 113835 | 6.0% |
| o | 108417 | 5.7% |
| i | 105775 | 5.6% |
| r | 95378 | 5.0% |
| s | 80596 | 4.2% |
| h | 58171 | 3.1% |
| Other values (343) | 684468 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 1903923 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 272527 | 14.3% | |
| e | 152683 | 8.0% |
| n | 117118 | 6.2% |
| t | 114955 | 6.0% |
| a | 113835 | 6.0% |
| o | 108417 | 5.7% |
| i | 105775 | 5.6% |
| r | 95378 | 5.0% |
| s | 80596 | 4.2% |
| h | 58171 | 3.1% |
| Other values (343) | 684468 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 1903923 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 272527 | 14.3% | |
| e | 152683 | 8.0% |
| n | 117118 | 6.2% |
| t | 114955 | 6.0% |
| a | 113835 | 6.0% |
| o | 108417 | 5.7% |
| i | 105775 | 5.6% |
| r | 95378 | 5.0% |
| s | 80596 | 4.2% |
| h | 58171 | 3.1% |
| Other values (343) | 684468 |
References
Text
Missing
| Distinct | 11382 |
|---|---|
| Distinct (%) | 99.4% |
| Missing | 740 |
| Missing (%) | 6.1% |
| Memory size | 21.1 MiB |
Length
| Max length | 2254 |
|---|---|
| Median length | 1422 |
| Mean length | 1050.1524 |
| Min length | 9 |
Unique
| Unique | 11317 ? |
|---|---|
| Unique (%) | 98.8% |
Sample
| 1st row | Astin, Alexander W., Student involvement: A developmental theory for higher education, Journal of College Student Development, 40, 5, pp. 518-529, (1999); Atmatzidou, Soumela, Advancing students' computational thinking skills through educational robotics: A study on age and gender relevant differences, Robotics and Autonomous Systems, 75, pp. 661-670, (2016); Bacca, Jorge, Student engagement with mobile-based assessment systems: A survival analysis, Journal of Computer Assisted Learning, 37, 1, pp. 158-171, (2021); Melander Bowden, Helen, Problem-solving in collaborative game design practices: epistemic stance, affect, and engagement, Learning, Media and Technology, 44, 2, pp. 124-143, (2019); APA Handbook of Research Methods in Psychology Research Designs Quantitative Qualitative Neuropsychological and Biological, (2023); Buil, Isabel, Engagement in business simulation games: A self-system model of motivational development, British Journal of Educational Technology, 51, 1, pp. 297-311, (2020); Çakır, Recep, The effect of robotic coding education on preschoolers’ problem solving and creative thinking skills, Thinking Skills and Creativity, 40, (2021); Thinking Skills and Creativity, (2021); Chao, Poyao, Exploring students' computational practice, design and performance of problem-solving through a visual programming environment, Computers and Education, 95, pp. 202-215, (2016); undefined |
|---|---|
| 2nd row | Aldabe, Itziar, Semantic similarity measures for the generation of science tests in basque, IEEE Transactions on Learning Technologies, 7, 4, pp. 375-387, (2014); Ameen, Linda Talib, The Impact of Artificial Intelligence on Computational Thinking in Education at University, International Journal of Engineering Pedagogy, 14, 5, pp. 192-203, (2024); Angeli Valanides, Charoula Nicos, Investigating the effects of gender and scaffolding in developing preschool children’s computational thinking during problem-solving with Bee-Bots, Frontiers in Education, 7, (2023); Asunda, Paul A., Embracing Computational Thinking as an Impetus for Artificial Intelligence in Integrated STEM Disciplines through Engineering and Technology Education, Journal of Technology Education, 34, 2, pp. 43-63, (2023); Atkinson, Richard C., Human Memory: A Proposed System and its Control Processes, Psychology of Learning and Motivation - Advances in Research and Theory, 2, C, pp. 89-195, (1968); Jbi Manual for Evidence Synthesis, (2024); Educ AI Tion Rebooted Exploring the Future of Artificial Intelligence in Schools and Colleges, (2019); Basu, Satabdi, Learner modeling for adaptive scaffolding in a Computational Thinking-based science learning environment, User Modeling and User-Adapted Interaction, 27, 1, pp. 5-53, (2017); Bhatt, Sohum Mandar, A Method for Developing Process-Based Assessments for Computational Thinking Tasks, Journal of Learning Analytics, 11, 2, pp. 157-173, (2024); Belland, Brian R., A Bayesian Network Meta-Analysis to Synthesize the Influence of Contexts of Scaffolding Use on Cognitive Outcomes in STEM Education, Review of Educational Research, 87, 6, pp. 1042-1081, (2017) |
| 3rd row | Alsadoon, Elham, Effects of a gamified learning environment on students’ achievement, motivations, and satisfaction, Heliyon, 8, 8, (2022); Journal of Languages and Language Teaching, (2023); Avcı, Canan, Computational thinking: early childhood teachers’ and prospective teachers’ preconceptions and self-efficacy, Education and Information Technologies, 27, 8, pp. 11689-11713, (2022); Bakeman, Roger A., Observer agreement for timed-event sequential data: A comparison of time-based and event-based algorithms, Behavior Research Methods, 41, 1, pp. 137-147, (2009); Bers, Marina Umaschi, Computational thinking and tinkering: Exploration of an early childhood robotics curriculum, Computers and Education, 72, pp. 145-157, (2014); Annual American Educational Research Association Meeting, (2012); Chao, Poyao, Exploring students' computational practice, design and performance of problem-solving through a visual programming environment, Computers and Education, 95, pp. 202-215, (2016); Cheng, Shuchen, Facilitating creativity, collaboration, and computational thinking in group website design: a concept mapping-based mobile flipped learning approach, International Journal of Mobile Learning and Organisation, 18, 2, pp. 169-193, (2024); Cheng, Yuping, Enhancing student's computational thinking skills with student-generated questions strategy in a game-based learning platform, Computers and Education, 200, (2023); Chevalier, Morgane, The role of feedback and guidance as intervention methods to foster computational thinking in educational robotics learning activities for primary school, Computers and Education, 180, (2022) |
| 4th row | Turkish Studies Educational Sciences, (2020); Mathematical Modelling Education in East and West, (2021); Journal of Theory and Practice in Education, (2017); Barr, Valerie B., Bringing computational thinking to K-12: What is involved and what is the role of the computer science education community?, ACM Inroads, 2, 1, pp. 48-54, (2011); Mathematical Epistemology and Psychology, (1966); Journal of Mathematical Modelling and Application, (2009); Modelling and Applications in Mathematics Education, (2007); Mathematical Modelling Ictma 12 Education Engineering and Economics, (2007); Modelling Applications and Applied Problem Solving, (1989); Borromeo-Ferri, Rita, Theoretical and empirical differentiations of phases in the modelling process, ZDM - International Journal on Mathematics Education, 38, 2, pp. 86-95, (2006) |
| 5th row | undefined, (2022); Iclr2022 Workshop on the Elements of Reasoning Objects Structure and Causality, (2022); undefined, (2025); Battaglia, Peter W., Interaction networks for learning about objects, relations and physics, Advances in Neural Information Processing Systems, pp. 4509-4517, (2016); Battaglia, Peter W., Simulation as an engine of physical scene understanding, Proceedings of the National Academy of Sciences of the United States of America, 110, 45, pp. 18327-18332, (2013); van Bergen, Ruben S., Object-Based Active Inference, Communications in Computer and Information Science, 1721 CCIS, pp. 50-64, (2023); Bas, Fred, Free Energy Principle for State and Input Estimation of a Quadcopter Flying in Wind, Proceedings - IEEE International Conference on Robotics and Automation, 2022-January, pp. 5389-5395, (2022); Cowley, Stephen John, How human infants deal with symbol grounding, Interaction Studies, 8, 1, pp. 83-104, (2007); undefined, (2022); Driess, Danny, Learning Multi-Object Dynamics with Compositional Neural Radiance Fields, Proceedings of Machine Learning Research, 205, pp. 1755-1768, (2023) |
| Value | Count | Frequency (%) |
| of | 63056 | 3.9% |
| and | 52726 | 3.3% |
| pp | 51469 | 3.2% |
| the | 37719 | 2.3% |
| in | 36346 | 2.2% |
| a | 24847 | 1.5% |
| education | 23077 | 1.4% |
| for | 18994 | 1.2% |
| thinking | 17393 | 1.1% |
| computational | 17173 | 1.1% |
| Other values (90024) | 1274852 |
Most occurring characters
| Value | Count | Frequency (%) |
| 1606002 | 13.4% | |
| e | 805206 | 6.7% |
| n | 785044 | 6.5% |
| i | 719409 | 6.0% |
| a | 673023 | 5.6% |
| o | 653918 | 5.4% |
| t | 575011 | 4.8% |
| r | 482124 | 4.0% |
| , | 429102 | 3.6% |
| s | 392720 | 3.3% |
| Other values (255) | 4904786 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 12026345 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 1606002 | 13.4% | |
| e | 805206 | 6.7% |
| n | 785044 | 6.5% |
| i | 719409 | 6.0% |
| a | 673023 | 5.6% |
| o | 653918 | 5.4% |
| t | 575011 | 4.8% |
| r | 482124 | 4.0% |
| , | 429102 | 3.6% |
| s | 392720 | 3.3% |
| Other values (255) | 4904786 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 12026345 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 1606002 | 13.4% | |
| e | 805206 | 6.7% |
| n | 785044 | 6.5% |
| i | 719409 | 6.0% |
| a | 673023 | 5.6% |
| o | 653918 | 5.4% |
| t | 575011 | 4.8% |
| r | 482124 | 4.0% |
| , | 429102 | 3.6% |
| s | 392720 | 3.3% |
| Other values (255) | 4904786 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 12026345 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 1606002 | 13.4% | |
| e | 805206 | 6.7% |
| n | 785044 | 6.5% |
| i | 719409 | 6.0% |
| a | 673023 | 5.6% |
| o | 653918 | 5.4% |
| t | 575011 | 4.8% |
| r | 482124 | 4.0% |
| , | 429102 | 3.6% |
| s | 392720 | 3.3% |
| Other values (255) | 4904786 |
Missing
| Distinct | 6581 |
|---|---|
| Distinct (%) | 95.5% |
| Missing | 5302 |
| Missing (%) | 43.5% |
| Memory size | 1.6 MiB |
Length
| Max length | 899 |
|---|---|
| Median length | 327 |
| Mean length | 132.69434 |
| Min length | 2 |
Unique
| Unique | 6355 ? |
|---|---|
| Unique (%) | 92.2% |
Sample
| 1st row | Y. Wang; Adolescent Education and Intelligence Support Lab of Nanjing Normal University, Nanjing, China; email: wangyang@nnu.edu.cn |
|---|---|
| 2nd row | Y. Yang; Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, No. 152 Luoyu Road, Hubei, 430079, China; email: yangyuqin@ccnu.edu.cn |
| 3rd row | T.-P. Hsu; Department of Technology Application and Human Resource Development, National Taiwan Normal University, Taipei city, 162, Sec. 1, East Heping Rd, 10610, Taiwan; email: 81171002H@ntnu.edu.tw |
| 4th row | F.K. Mumcu; Digitalization in Early Childhood Education, Department of Education Research and Teacher Education, University of Graz, Graz, Austria; email: filiz.mumcu@uni-graz.at |
| 5th row | P. Lanillos; Donders Institute, Radboud University, Nijmegen, Netherlands; email: p.lanillos@csic.es |
| Value | Count | Frequency (%) |
| of | 7282 | 6.8% |
| 7108 | 6.6% | |
| university | 5229 | 4.9% |
| and | 2337 | 2.2% |
| department | 2190 | 2.0% |
| united | 1945 | 1.8% |
| states | 1638 | 1.5% |
| education | 1453 | 1.4% |
| science | 1146 | 1.1% |
| china | 1110 | 1.0% |
| Other values (19556) | 75991 |
Most occurring characters
| Value | Count | Frequency (%) |
| 100552 | 11.0% | |
| e | 68670 | 7.5% |
| a | 66319 | 7.3% |
| i | 62711 | 6.9% |
| n | 57927 | 6.3% |
| t | 43799 | 4.8% |
| o | 43783 | 4.8% |
| r | 35041 | 3.8% |
| l | 31460 | 3.4% |
| s | 27553 | 3.0% |
| Other values (156) | 376449 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 914264 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 100552 | 11.0% | |
| e | 68670 | 7.5% |
| a | 66319 | 7.3% |
| i | 62711 | 6.9% |
| n | 57927 | 6.3% |
| t | 43799 | 4.8% |
| o | 43783 | 4.8% |
| r | 35041 | 3.8% |
| l | 31460 | 3.4% |
| s | 27553 | 3.0% |
| Other values (156) | 376449 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 914264 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 100552 | 11.0% | |
| e | 68670 | 7.5% |
| a | 66319 | 7.3% |
| i | 62711 | 6.9% |
| n | 57927 | 6.3% |
| t | 43799 | 4.8% |
| o | 43783 | 4.8% |
| r | 35041 | 3.8% |
| l | 31460 | 3.4% |
| s | 27553 | 3.0% |
| Other values (156) | 376449 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 914264 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 100552 | 11.0% | |
| e | 68670 | 7.5% |
| a | 66319 | 7.3% |
| i | 62711 | 6.9% |
| n | 57927 | 6.3% |
| t | 43799 | 4.8% |
| o | 43783 | 4.8% |
| r | 35041 | 3.8% |
| l | 31460 | 3.4% |
| s | 27553 | 3.0% |
| Other values (156) | 376449 |
Editors
Text
Missing
| Distinct | 1266 |
|---|---|
| Distinct (%) | 45.9% |
| Missing | 9435 |
| Missing (%) | 77.4% |
| Memory size | 607.2 KiB |
Length
| Max length | 475 |
|---|---|
| Median length | 174 |
| Mean length | 61.002176 |
| Min length | 4 |
Unique
| Unique | 812 ? |
|---|---|
| Unique (%) | 29.5% |
Sample
| 1st row | Cheung, S.K.S.; Liu, X.; Xu, G.; Kwok, L.-F. |
|---|---|
| 2nd row | Liu, M.; Yu, X.; Xu, C.; Song, Y. |
| 3rd row | Tammets, K.; Sosnovsky, S.; Ferreira Mello, R.; Pishtari, G.; Nazaretsky, T. |
| 4th row | Tammets, K.; Sosnovsky, S.; Ferreira Mello, R.; Pishtari, G.; Nazaretsky, T. |
| 5th row | Zhu, T.; Zhou, W.; Zhu, C. |
| Value | Count | Frequency (%) |
| m | 1099 | 4.1% |
| j | 934 | 3.5% |
| a | 795 | 3.0% |
| s | 610 | 2.3% |
| r | 501 | 1.9% |
| d | 494 | 1.8% |
| c | 460 | 1.7% |
| t | 408 | 1.5% |
| g | 351 | 1.3% |
| p | 340 | 1.3% |
| Other values (3938) | 20745 |
Most occurring characters
| Value | Count | Frequency (%) |
| 23980 | 14.3% | |
| . | 17564 | 10.4% |
| , | 13118 | 7.8% |
| ; | 10428 | 6.2% |
| a | 8802 | 5.2% |
| e | 6132 | 3.6% |
| n | 5910 | 3.5% |
| i | 5836 | 3.5% |
| o | 5050 | 3.0% |
| r | 4916 | 2.9% |
| Other values (86) | 66447 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 168183 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 23980 | 14.3% | |
| . | 17564 | 10.4% |
| , | 13118 | 7.8% |
| ; | 10428 | 6.2% |
| a | 8802 | 5.2% |
| e | 6132 | 3.6% |
| n | 5910 | 3.5% |
| i | 5836 | 3.5% |
| o | 5050 | 3.0% |
| r | 4916 | 2.9% |
| Other values (86) | 66447 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 168183 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 23980 | 14.3% | |
| . | 17564 | 10.4% |
| , | 13118 | 7.8% |
| ; | 10428 | 6.2% |
| a | 8802 | 5.2% |
| e | 6132 | 3.6% |
| n | 5910 | 3.5% |
| i | 5836 | 3.5% |
| o | 5050 | 3.0% |
| r | 4916 | 2.9% |
| Other values (86) | 66447 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 168183 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 23980 | 14.3% | |
| . | 17564 | 10.4% |
| , | 13118 | 7.8% |
| ; | 10428 | 6.2% |
| a | 8802 | 5.2% |
| e | 6132 | 3.6% |
| n | 5910 | 3.5% |
| i | 5836 | 3.5% |
| o | 5050 | 3.0% |
| r | 4916 | 2.9% |
| Other values (86) | 66447 |
Publisher
Text
Missing
| Distinct | 1115 |
|---|---|
| Distinct (%) | 10.1% |
| Missing | 1121 |
| Missing (%) | 9.2% |
| Memory size | 951.9 KiB |
Length
| Max length | 168 |
|---|---|
| Median length | 108 |
| Mean length | 35.738416 |
| Min length | 3 |
Unique
| Unique | 614 ? |
|---|---|
| Unique (%) | 5.5% |
Sample
| 1st row | Elsevier Ltd |
|---|---|
| 2nd row | Elsevier Ltd |
| 3rd row | Elsevier Ltd |
| 4th row | Elsevier Ltd |
| 5th row | Elsevier Ltd |
| Value | Count | Frequency (%) |
| and | 2913 | 5.8% |
| of | 2465 | 4.9% |
| inc | 2251 | 4.5% |
| springer | 1863 | 3.7% |
| for | 1860 | 3.7% |
| institute | 1674 | 3.4% |
| association | 1606 | 3.2% |
| computing | 1253 | 2.5% |
| engineers | 1252 | 2.5% |
| machinery | 1246 | 2.5% |
| Other values (1852) | 31476 |
Most occurring characters
| Value | Count | Frequency (%) |
| 38788 | 9.8% | |
| i | 33122 | 8.4% |
| n | 32105 | 8.1% |
| e | 30929 | 7.8% |
| c | 21670 | 5.5% |
| r | 21459 | 5.4% |
| t | 21037 | 5.3% |
| o | 20917 | 5.3% |
| a | 20601 | 5.2% |
| s | 19859 | 5.0% |
| Other values (72) | 135173 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 395660 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 38788 | 9.8% | |
| i | 33122 | 8.4% |
| n | 32105 | 8.1% |
| e | 30929 | 7.8% |
| c | 21670 | 5.5% |
| r | 21459 | 5.4% |
| t | 21037 | 5.3% |
| o | 20917 | 5.3% |
| a | 20601 | 5.2% |
| s | 19859 | 5.0% |
| Other values (72) | 135173 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 395660 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 38788 | 9.8% | |
| i | 33122 | 8.4% |
| n | 32105 | 8.1% |
| e | 30929 | 7.8% |
| c | 21670 | 5.5% |
| r | 21459 | 5.4% |
| t | 21037 | 5.3% |
| o | 20917 | 5.3% |
| a | 20601 | 5.2% |
| s | 19859 | 5.0% |
| Other values (72) | 135173 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 395660 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 38788 | 9.8% | |
| i | 33122 | 8.4% |
| n | 32105 | 8.1% |
| e | 30929 | 7.8% |
| c | 21670 | 5.5% |
| r | 21459 | 5.4% |
| t | 21037 | 5.3% |
| o | 20917 | 5.3% |
| a | 20601 | 5.2% |
| s | 19859 | 5.0% |
| Other values (72) | 135173 |
Sponsors
Text
Missing
| Distinct | 855 |
|---|---|
| Distinct (%) | 33.9% |
| Missing | 9667 |
| Missing (%) | 79.3% |
| Memory size | 631.9 KiB |
Length
| Max length | 355 |
|---|---|
| Median length | 230 |
| Mean length | 79.124356 |
| Min length | 3 |
Unique
| Unique | 574 ? |
|---|---|
| Unique (%) | 22.7% |
Sample
| 1st row | Hong Kong Pei Hua Education Foundation |
|---|---|
| 2nd row | Australian National University# Google# Monash University# CSIRO-Data61# Pioneer# Yep AI# Australian Computer Society# Defence Artificial Intelligence Research Network (DAIRNET)# Follow Me AI# Computing Research and Education Association of Australasia# UNSW AI Institute# Springer# |
| 3rd row | RedUNCI#National University of La Plata#CIC#CONICET La Plata#National Engineering Academy#PoloITLaPlata |
| 4th row | RedUNCI#National University of La Plata#CIC#CONICET La Plata#National Engineering Academy#PoloITLaPlata |
| 5th row | ifip#Sociedade Brasileira de Computacao#FAPEMIG#CNPq#CAPES |
| Value | Count | Frequency (%) |
| of | 1065 | 4.1% |
| acm | 1022 | 3.9% |
| ieee | 890 | 3.4% |
| society | 817 | 3.1% |
| university | 782 | 3.0% |
| and | 740 | 2.8% |
| education | 729 | 2.8% |
| computer | 645 | 2.5% |
| sigcse | 561 | 2.1% |
| for | 542 | 2.1% |
| Other values (2548) | 18382 |
Most occurring characters
| Value | Count | Frequency (%) |
| 23650 | 11.8% | |
| e | 14673 | 7.3% |
| n | 13528 | 6.8% |
| i | 13350 | 6.7% |
| o | 12525 | 6.3% |
| t | 11164 | 5.6% |
| a | 9974 | 5.0% |
| r | 8231 | 4.1% |
| c | 7437 | 3.7% |
| E | 6090 | 3.0% |
| Other values (75) | 79167 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 199789 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 23650 | 11.8% | |
| e | 14673 | 7.3% |
| n | 13528 | 6.8% |
| i | 13350 | 6.7% |
| o | 12525 | 6.3% |
| t | 11164 | 5.6% |
| a | 9974 | 5.0% |
| r | 8231 | 4.1% |
| c | 7437 | 3.7% |
| E | 6090 | 3.0% |
| Other values (75) | 79167 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 199789 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 23650 | 11.8% | |
| e | 14673 | 7.3% |
| n | 13528 | 6.8% |
| i | 13350 | 6.7% |
| o | 12525 | 6.3% |
| t | 11164 | 5.6% |
| a | 9974 | 5.0% |
| r | 8231 | 4.1% |
| c | 7437 | 3.7% |
| E | 6090 | 3.0% |
| Other values (75) | 79167 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 199789 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 23650 | 11.8% | |
| e | 14673 | 7.3% |
| n | 13528 | 6.8% |
| i | 13350 | 6.7% |
| o | 12525 | 6.3% |
| t | 11164 | 5.6% |
| a | 9974 | 5.0% |
| r | 8231 | 4.1% |
| c | 7437 | 3.7% |
| E | 6090 | 3.0% |
| Other values (75) | 79167 |
Conference name
Text
Missing
| Distinct | 2734 |
|---|---|
| Distinct (%) | 48.1% |
| Missing | 6503 |
| Missing (%) | 53.3% |
| Memory size | 940.5 KiB |
Length
| Max length | 407 |
|---|---|
| Median length | 224 |
| Mean length | 83.482686 |
| Min length | 9 |
Unique
| Unique | 1890 ? |
|---|---|
| Unique (%) | 33.2% |
Sample
| 1st row | 8th International Conference on Technology in Education, ICTE 2025 |
|---|---|
| 2nd row | 38th Australasian Joint Conference on Artificial Intelligence, AI 2025 |
| 3rd row | 20th European Conference on Technology Enhanced Learning, EC-TEL 2025 |
| 4th row | 20th European Conference on Technology Enhanced Learning, EC-TEL 2025 |
| 5th row | 18th International Conference on Knowledge Science, Engineering and Management, KSEM 2025 |
| Value | Count | Frequency (%) |
| conference | 4625 | 7.3% |
| on | 4132 | 6.6% |
| international | 3369 | 5.4% |
| and | 2979 | 4.7% |
| education | 2215 | 3.5% |
| in | 1492 | 2.4% |
| computer | 929 | 1.5% |
| of | 875 | 1.4% |
| the | 864 | 1.4% |
| science | 829 | 1.3% |
| Other values (3112) | 40659 |
Most occurring characters
| Value | Count | Frequency (%) |
| 57279 | 12.1% | |
| n | 50401 | 10.6% |
| e | 36402 | 7.7% |
| o | 30492 | 6.4% |
| t | 26213 | 5.5% |
| i | 25233 | 5.3% |
| a | 23901 | 5.0% |
| r | 18725 | 3.9% |
| c | 16268 | 3.4% |
| C | 14105 | 3.0% |
| Other values (73) | 175914 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 474933 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 57279 | 12.1% | |
| n | 50401 | 10.6% |
| e | 36402 | 7.7% |
| o | 30492 | 6.4% |
| t | 26213 | 5.5% |
| i | 25233 | 5.3% |
| a | 23901 | 5.0% |
| r | 18725 | 3.9% |
| c | 16268 | 3.4% |
| C | 14105 | 3.0% |
| Other values (73) | 175914 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 474933 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 57279 | 12.1% | |
| n | 50401 | 10.6% |
| e | 36402 | 7.7% |
| o | 30492 | 6.4% |
| t | 26213 | 5.5% |
| i | 25233 | 5.3% |
| a | 23901 | 5.0% |
| r | 18725 | 3.9% |
| c | 16268 | 3.4% |
| C | 14105 | 3.0% |
| Other values (73) | 175914 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 474933 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 57279 | 12.1% | |
| n | 50401 | 10.6% |
| e | 36402 | 7.7% |
| o | 30492 | 6.4% |
| t | 26213 | 5.5% |
| i | 25233 | 5.3% |
| a | 23901 | 5.0% |
| r | 18725 | 3.9% |
| c | 16268 | 3.4% |
| C | 14105 | 3.0% |
| Other values (73) | 175914 |
Conference date
Text
Missing
| Distinct | 1876 |
|---|---|
| Distinct (%) | 38.1% |
| Missing | 7267 |
| Missing (%) | 59.6% |
| Memory size | 599.3 KiB |
Length
| Max length | 29 |
|---|---|
| Median length | 29 |
| Mean length | 28.351878 |
| Min length | 10 |
Unique
| Unique | 1085 ? |
|---|---|
| Unique (%) | 22.0% |
Sample
| 1st row | 2025-12-10 through 2025-12-12 |
|---|---|
| 2nd row | 2025-12-01 through 2025-12-05 |
| 3rd row | 2025-09-15 through 2025-09-19 |
| 4th row | 2025-09-15 through 2025-09-19 |
| 5th row | 2025-08-04 through 2025-08-07 |
| Value | Count | Frequency (%) |
| through | 4757 | |
| 2019-06-15 | 65 | 0.5% |
| 2020-03-11 | 48 | 0.3% |
| 2019-06-13 | 44 | 0.3% |
| 2020-03-14 | 40 | 0.3% |
| 2024-05-30 | 40 | 0.3% |
| 2020-06-19 | 39 | 0.3% |
| 2024-05-28 | 38 | 0.3% |
| 2024-03-20 | 35 | 0.2% |
| 2024-03-23 | 35 | 0.2% |
| Other values (2399) | 9298 |
Most occurring characters
| Value | Count | Frequency (%) |
| 0 | 22133 | |
| 2 | 21870 | |
| - | 19364 | |
| 1 | 13210 | |
| h | 9514 | 6.8% |
| 9514 | 6.8% | |
| t | 4757 | 3.4% |
| u | 4757 | 3.4% |
| o | 4757 | 3.4% |
| r | 4757 | 3.4% |
| Other values (8) | 25000 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 139633 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 0 | 22133 | |
| 2 | 21870 | |
| - | 19364 | |
| 1 | 13210 | |
| h | 9514 | 6.8% |
| 9514 | 6.8% | |
| t | 4757 | 3.4% |
| u | 4757 | 3.4% |
| o | 4757 | 3.4% |
| r | 4757 | 3.4% |
| Other values (8) | 25000 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 139633 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 0 | 22133 | |
| 2 | 21870 | |
| - | 19364 | |
| 1 | 13210 | |
| h | 9514 | 6.8% |
| 9514 | 6.8% | |
| t | 4757 | 3.4% |
| u | 4757 | 3.4% |
| o | 4757 | 3.4% |
| r | 4757 | 3.4% |
| Other values (8) | 25000 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 139633 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 0 | 22133 | |
| 2 | 21870 | |
| - | 19364 | |
| 1 | 13210 | |
| h | 9514 | 6.8% |
| 9514 | 6.8% | |
| t | 4757 | 3.4% |
| u | 4757 | 3.4% |
| o | 4757 | 3.4% |
| r | 4757 | 3.4% |
| Other values (8) | 25000 |
Conference location
Text
Missing
| Distinct | 875 |
|---|---|
| Distinct (%) | 17.8% |
| Missing | 7265 |
| Missing (%) | 59.6% |
| Memory size | 508.5 KiB |
Length
| Max length | 38 |
|---|---|
| Median length | 27 |
| Mean length | 9.2689263 |
| Min length | 3 |
Unique
| Unique | 377 ? |
|---|---|
| Unique (%) | 7.7% |
Sample
| 1st row | Shenzhen |
|---|---|
| 2nd row | Canberra |
| 3rd row | Newcastle upon Tyne |
| 4th row | Newcastle upon Tyne |
| 5th row | Macao |
| Value | Count | Frequency (%) |
| virtual | 664 | 9.9% |
| online | 602 | 9.0% |
| kong | 176 | 2.6% |
| hong | 176 | 2.6% |
| hybrid | 141 | 2.1% |
| portland | 101 | 1.5% |
| london | 72 | 1.1% |
| city | 66 | 1.0% |
| san | 65 | 1.0% |
| beijing | 59 | 0.9% |
| Other values (877) | 4584 |
Most occurring characters
| Value | Count | Frequency (%) |
| a | 4683 | 10.3% |
| n | 4399 | 9.6% |
| i | 3598 | 7.9% |
| e | 2958 | 6.5% |
| l | 2927 | 6.4% |
| o | 2922 | 6.4% |
| r | 2619 | 5.7% |
| t | 2081 | 4.6% |
| u | 1896 | 4.2% |
| 1779 | 3.9% | |
| Other values (58) | 15806 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 45668 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| a | 4683 | 10.3% |
| n | 4399 | 9.6% |
| i | 3598 | 7.9% |
| e | 2958 | 6.5% |
| l | 2927 | 6.4% |
| o | 2922 | 6.4% |
| r | 2619 | 5.7% |
| t | 2081 | 4.6% |
| u | 1896 | 4.2% |
| 1779 | 3.9% | |
| Other values (58) | 15806 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 45668 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| a | 4683 | 10.3% |
| n | 4399 | 9.6% |
| i | 3598 | 7.9% |
| e | 2958 | 6.5% |
| l | 2927 | 6.4% |
| o | 2922 | 6.4% |
| r | 2619 | 5.7% |
| t | 2081 | 4.6% |
| u | 1896 | 4.2% |
| 1779 | 3.9% | |
| Other values (58) | 15806 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 45668 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| a | 4683 | 10.3% |
| n | 4399 | 9.6% |
| i | 3598 | 7.9% |
| e | 2958 | 6.5% |
| l | 2927 | 6.4% |
| o | 2922 | 6.4% |
| r | 2619 | 5.7% |
| t | 2081 | 4.6% |
| u | 1896 | 4.2% |
| 1779 | 3.9% | |
| Other values (58) | 15806 |
Conference code
Real number (ℝ)
High correlation Missing
| Distinct | 2215 |
|---|---|
| Distinct (%) | 45.0% |
| Missing | 7267 |
| Missing (%) | 59.6% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 193218.46 |
| Minimum | 0 |
|---|---|
| Maximum | 344589 |
| Zeros | 1 |
| Zeros (%) | < 0.1% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 95.4 KiB |
Quantile statistics
| Minimum | 0 |
|---|---|
| 5-th percentile | 121037 |
| Q1 | 154355 |
| median | 185019 |
| Q3 | 209971 |
| 95-th percentile | 315263 |
| Maximum | 344589 |
| Range | 344589 |
| Interquartile range (IQR) | 55616 |
Descriptive statistics
| Standard deviation | 55958.228 |
|---|---|
| Coefficient of variation (CV) | 0.28961119 |
| Kurtosis | 0.2795209 |
| Mean | 193218.46 |
| Median Absolute Deviation (MAD) | 27925 |
| Skewness | 0.91663148 |
| Sum | 9.516009 × 108 |
| Variance | 3.1313232 × 109 |
| Monotonicity | Not monotonic |
| Value | Count | Frequency (%) |
| 249739 | 43 | 0.4% |
| 157964 | 39 | 0.3% |
| 318479 | 38 | 0.3% |
| 197936 | 35 | 0.3% |
| 249719 | 33 | 0.3% |
| 208773 | 32 | 0.3% |
| 249749 | 31 | 0.3% |
| 290089 | 30 | 0.2% |
| 249729 | 29 | 0.2% |
| 145475 | 27 | 0.2% |
| Other values (2205) | 4588 | |
| (Missing) | 7267 |
| Value | Count | Frequency (%) |
| 0 | 1 | |
| 107282 | 1 | |
| 107460 | 1 | |
| 107729 | 2 | |
| 108117 | 1 | |
| 108502 | 1 | |
| 108554 | 1 | |
| 108593 | 1 | |
| 108664 | 2 | |
| 108759 | 1 |
| Value | Count | Frequency (%) |
| 344589 | 1 | |
| 344239 | 1 | |
| 344219 | 1 | |
| 343759 | 1 | |
| 343719 | 1 | |
| 343679 | 1 | |
| 343559 | 1 | |
| 343109 | 1 | |
| 342789 | 1 | |
| 342649 | 1 |
ISSN
Text
Missing
| Distinct | 1901 |
|---|---|
| Distinct (%) | 26.2% |
| Missing | 4939 |
| Missing (%) | 40.5% |
| Memory size | 561.1 KiB |
Length
| Max length | 18 |
|---|---|
| Median length | 8 |
| Mean length | 8.4108645 |
| Min length | 8 |
Unique
| Unique | 1214 ? |
|---|---|
| Unique (%) | 16.7% |
Sample
| 1st row | 18711871 |
|---|---|
| 2nd row | 18711871 |
| 3rd row | 18711871 |
| 4th row | 18711871 |
| 5th row | 08936080 |
| Value | Count | Frequency (%) |
| 03029743 | 427 | 5.7% |
| 21531633 | 345 | 4.6% |
| 01905848 | 178 | 2.4% |
| 15394565 | 178 | 2.4% |
| 13602357 | 167 | 2.2% |
| 18650929 | 157 | 2.1% |
| 1942647x | 135 | 1.8% |
| 18149316 | 128 | 1.7% |
| 16130073 | 118 | 1.6% |
| 23673370 | 95 | 1.3% |
| Other values (1933) | 5623 |
Most occurring characters
| Value | Count | Frequency (%) |
| 1 | 8522 | |
| 0 | 8434 | |
| 3 | 7039 | |
| 2 | 6191 | |
| 5 | 5349 | |
| 6 | 5169 | |
| 9 | 5126 | |
| 4 | 4878 | |
| 7 | 4697 | |
| 8 | 4247 | |
| Other values (3) | 1352 | 2.2% |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 61004 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 1 | 8522 | |
| 0 | 8434 | |
| 3 | 7039 | |
| 2 | 6191 | |
| 5 | 5349 | |
| 6 | 5169 | |
| 9 | 5126 | |
| 4 | 4878 | |
| 7 | 4697 | |
| 8 | 4247 | |
| Other values (3) | 1352 | 2.2% |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 61004 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 1 | 8522 | |
| 0 | 8434 | |
| 3 | 7039 | |
| 2 | 6191 | |
| 5 | 5349 | |
| 6 | 5169 | |
| 9 | 5126 | |
| 4 | 4878 | |
| 7 | 4697 | |
| 8 | 4247 | |
| Other values (3) | 1352 | 2.2% |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 61004 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 1 | 8522 | |
| 0 | 8434 | |
| 3 | 7039 | |
| 2 | 6191 | |
| 5 | 5349 | |
| 6 | 5169 | |
| 9 | 5126 | |
| 4 | 4878 | |
| 7 | 4697 | |
| 8 | 4247 | |
| Other values (3) | 1352 | 2.2% |
ISBN
Text
Missing
| Distinct | 2078 |
|---|---|
| Distinct (%) | 31.5% |
| Missing | 5604 |
| Missing (%) | 46.0% |
| Memory size | 962.6 KiB |
Length
| Max length | 148 |
|---|---|
| Median length | 145 |
| Mean length | 73.384942 |
| Min length | 10 |
Unique
| Unique | 1487 ? |
|---|---|
| Unique (%) | 22.6% |
Sample
| 1st row | 008030270X; 9780080302706 |
|---|---|
| 2nd row | 008031418X; 0080323723; 9780080323725 |
| 3rd row | 9781394352821; 9781394352814 |
| 4th row | 9789819671748; 9789819664610; 9783032026743; 9783032008831; 9783032026712; 9789819671779; 9783031949425; 9789819666874; 9783031936968; 9783031941207 |
| 5th row | 9789819698936; 9789819698042; 9789819698110; 9789819698905; 9783032004949; 9789819512324; 9783032026019; 9783032008909; 9783031915802; 9789819698141 |
| Value | Count | Frequency (%) |
| 9789819698936 | 427 | 1.3% |
| 9789819698042 | 427 | 1.3% |
| 9789819698905 | 427 | 1.3% |
| 9789819698110 | 427 | 1.3% |
| 9789819698141 | 427 | 1.3% |
| 9789819512324 | 427 | 1.3% |
| 9783032026019 | 427 | 1.3% |
| 9783032008909 | 427 | 1.3% |
| 9783032004949 | 427 | 1.3% |
| 9783031915802 | 427 | 1.3% |
| Other values (5010) | 29313 |
Most occurring characters
| Value | Count | Frequency (%) |
| 9 | 71497 | |
| 8 | 62725 | |
| 7 | 57715 | |
| 0 | 45863 | |
| 1 | 43544 | |
| 3 | 41773 | |
| 4 | 31717 | |
| 26995 | 5.6% | |
| ; | 26994 | 5.6% |
| 5 | 26077 | 5.4% |
| Other values (3) | 48560 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 483460 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 9 | 71497 | |
| 8 | 62725 | |
| 7 | 57715 | |
| 0 | 45863 | |
| 1 | 43544 | |
| 3 | 41773 | |
| 4 | 31717 | |
| 26995 | 5.6% | |
| ; | 26994 | 5.6% |
| 5 | 26077 | 5.4% |
| Other values (3) | 48560 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 483460 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 9 | 71497 | |
| 8 | 62725 | |
| 7 | 57715 | |
| 0 | 45863 | |
| 1 | 43544 | |
| 3 | 41773 | |
| 4 | 31717 | |
| 26995 | 5.6% | |
| ; | 26994 | 5.6% |
| 5 | 26077 | 5.4% |
| Other values (3) | 48560 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 483460 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 9 | 71497 | |
| 8 | 62725 | |
| 7 | 57715 | |
| 0 | 45863 | |
| 1 | 43544 | |
| 3 | 41773 | |
| 4 | 31717 | |
| 26995 | 5.6% | |
| ; | 26994 | 5.6% |
| 5 | 26077 | 5.4% |
| Other values (3) | 48560 |
CODEN
Text
Missing
| Distinct | 599 |
|---|---|
| Distinct (%) | 35.5% |
| Missing | 10506 |
| Missing (%) | 86.2% |
| Memory size | 417.4 KiB |
Length
| Max length | 5 |
|---|---|
| Median length | 5 |
| Mean length | 5 |
| Min length | 5 |
Unique
| Unique | 373 ? |
|---|---|
| Unique (%) | 22.1% |
Sample
| 1st row | NNETE |
|---|---|
| 2nd row | COMED |
| 3rd row | NNETE |
| 4th row | MIAEC |
| 5th row | CCEND |
| Value | Count | Frequency (%) |
| pfecd | 178 | 10.6% |
| comed | 63 | 3.7% |
| capee | 49 | 2.9% |
| cacma | 39 | 2.3% |
| icmlf | 35 | 2.1% |
| psisd | 28 | 1.7% |
| chbee | 27 | 1.6% |
| bjetd | 27 | 1.6% |
| tcscf | 24 | 1.4% |
| cgtna | 23 | 1.4% |
| Other values (589) | 1193 |
Most occurring characters
| Value | Count | Frequency (%) |
| E | 1233 | |
| C | 1043 | |
| D | 724 | 8.6% |
| A | 723 | 8.6% |
| P | 565 | 6.7% |
| S | 521 | 6.2% |
| I | 485 | 5.8% |
| F | 406 | 4.8% |
| M | 380 | 4.5% |
| B | 336 | 4.0% |
| Other values (26) | 2014 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 8430 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| E | 1233 | |
| C | 1043 | |
| D | 724 | 8.6% |
| A | 723 | 8.6% |
| P | 565 | 6.7% |
| S | 521 | 6.2% |
| I | 485 | 5.8% |
| F | 406 | 4.8% |
| M | 380 | 4.5% |
| B | 336 | 4.0% |
| Other values (26) | 2014 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 8430 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| E | 1233 | |
| C | 1043 | |
| D | 724 | 8.6% |
| A | 723 | 8.6% |
| P | 565 | 6.7% |
| S | 521 | 6.2% |
| I | 485 | 5.8% |
| F | 406 | 4.8% |
| M | 380 | 4.5% |
| B | 336 | 4.0% |
| Other values (26) | 2014 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 8430 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| E | 1233 | |
| C | 1043 | |
| D | 724 | 8.6% |
| A | 723 | 8.6% |
| P | 565 | 6.7% |
| S | 521 | 6.2% |
| I | 485 | 5.8% |
| F | 406 | 4.8% |
| M | 380 | 4.5% |
| B | 336 | 4.0% |
| Other values (26) | 2014 |
PubMed ID
Real number (ℝ)
High correlation Missing
| Distinct | 664 |
|---|---|
| Distinct (%) | 100.0% |
| Missing | 11528 |
| Missing (%) | 94.6% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 29006954 |
| Minimum | 1488649 |
|---|---|
| Maximum | 41337481 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 95.4 KiB |
Quantile statistics
| Minimum | 1488649 |
|---|---|
| 5-th percentile | 12537762 |
| Q1 | 23593944 |
| median | 30134232 |
| Q3 | 35461219 |
| 95-th percentile | 40299276 |
| Maximum | 41337481 |
| Range | 39848832 |
| Interquartile range (IQR) | 11867276 |
Descriptive statistics
| Standard deviation | 8215618 |
|---|---|
| Coefficient of variation (CV) | 0.28322926 |
| Kurtosis | -0.073074655 |
| Mean | 29006954 |
| Median Absolute Deviation (MAD) | 5853908 |
| Skewness | -0.65109785 |
| Sum | 1.9260617 × 1010 |
| Variance | 6.7496379 × 1013 |
| Monotonicity | Not monotonic |
| Value | Count | Frequency (%) |
| 25900134 | 1 | < 0.1% |
| 26041580 | 1 | < 0.1% |
| 25824671 | 1 | < 0.1% |
| 25721105 | 1 | < 0.1% |
| 25902473 | 1 | < 0.1% |
| 25880064 | 1 | < 0.1% |
| 25772159 | 1 | < 0.1% |
| 25726919 | 1 | < 0.1% |
| 25898807 | 1 | < 0.1% |
| 25867996 | 1 | < 0.1% |
| Other values (654) | 654 | 5.4% |
| (Missing) | 11528 |
| Value | Count | Frequency (%) |
| 1488649 | 1 | |
| 1546114 | 1 | |
| 6543165 | 1 | |
| 6645214 | 1 | |
| 7501130 | 1 | |
| 7724567 | 1 | |
| 8022956 | 1 | |
| 8262029 | 1 | |
| 8364533 | 1 | |
| 8374068 | 1 |
| Value | Count | Frequency (%) |
| 41337481 | 1 | |
| 41315544 | 1 | |
| 41305265 | 1 | |
| 41204976 | 1 | |
| 41094117 | 1 | |
| 41053358 | 1 | |
| 41047156 | 1 | |
| 41043141 | 1 | |
| 41020363 | 1 | |
| 41006537 | 1 |
Language of Original Document
Categorical
Imbalance
| Distinct | 17 |
|---|---|
| Distinct (%) | 0.1% |
| Missing | 1 |
| Missing (%) | < 0.1% |
| Memory size | 667.0 KiB |
| English | |
|---|---|
| Spanish | 159 |
| Chinese | 149 |
| Portuguese | 50 |
| Russian | 24 |
| Other values (12) | 45 |
Length
| Max length | 10 |
|---|---|
| Median length | 7 |
| Mean length | 7.0113198 |
| Min length | 6 |
Unique
| Unique | 3 ? |
|---|---|
| Unique (%) | < 0.1% |
Sample
| 1st row | English |
|---|---|
| 2nd row | English |
| 3rd row | English |
| 4th row | English |
| 5th row | English |
Common Values
| Value | Count | Frequency (%) |
| English | 11764 | |
| Spanish | 159 | 1.3% |
| Chinese | 149 | 1.2% |
| Portuguese | 50 | 0.4% |
| Russian | 24 | 0.2% |
| Italian | 12 | 0.1% |
| German | 9 | 0.1% |
| Polish | 4 | < 0.1% |
| Japanese | 4 | < 0.1% |
| Croatian | 3 | < 0.1% |
| Other values (7) | 13 | 0.1% |
Length
| Value | Count | Frequency (%) |
| english | 11764 | |
| spanish | 159 | 1.3% |
| chinese | 149 | 1.2% |
| portuguese | 50 | 0.4% |
| russian | 24 | 0.2% |
| italian | 12 | 0.1% |
| german | 9 | 0.1% |
| polish | 4 | < 0.1% |
| japanese | 4 | < 0.1% |
| croatian | 3 | < 0.1% |
| Other values (7) | 13 | 0.1% |
Most occurring characters
| Value | Count | Frequency (%) |
| s | 12182 | |
| n | 12132 | |
| i | 12123 | |
| h | 12082 | |
| g | 11815 | |
| l | 11780 | |
| E | 11764 | |
| e | 422 | 0.5% |
| a | 238 | 0.3% |
| p | 163 | 0.2% |
| Other values (22) | 774 | 0.9% |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 85475 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| s | 12182 | |
| n | 12132 | |
| i | 12123 | |
| h | 12082 | |
| g | 11815 | |
| l | 11780 | |
| E | 11764 | |
| e | 422 | 0.5% |
| a | 238 | 0.3% |
| p | 163 | 0.2% |
| Other values (22) | 774 | 0.9% |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 85475 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| s | 12182 | |
| n | 12132 | |
| i | 12123 | |
| h | 12082 | |
| g | 11815 | |
| l | 11780 | |
| E | 11764 | |
| e | 422 | 0.5% |
| a | 238 | 0.3% |
| p | 163 | 0.2% |
| Other values (22) | 774 | 0.9% |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 85475 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| s | 12182 | |
| n | 12132 | |
| i | 12123 | |
| h | 12082 | |
| g | 11815 | |
| l | 11780 | |
| E | 11764 | |
| e | 422 | 0.5% |
| a | 238 | 0.3% |
| p | 163 | 0.2% |
| Other values (22) | 774 | 0.9% |
| Distinct | 3880 |
|---|---|
| Distinct (%) | 31.8% |
| Missing | 7 |
| Missing (%) | 0.1% |
| Memory size | 962.3 KiB |
Length
| Max length | 253 |
|---|---|
| Median length | 126 |
| Mean length | 31.761182 |
| Min length | 3 |
Unique
| Unique | 2635 ? |
|---|---|
| Unique (%) | 21.6% |
Sample
| 1st row | Think. Skills Creat. |
|---|---|
| 2nd row | Think. Skills Creat. |
| 3rd row | Think. Skills Creat. |
| 4th row | Think. Skills Creat. |
| 5th row | Neural Netw. |
| Value | Count | Frequency (%) |
| conf | 4005 | 6.3% |
| proc | 3715 | 5.9% |
| educ | 3246 | 5.1% |
| comput | 3218 | 5.1% |
| int | 2936 | 4.6% |
| sci | 2349 | 3.7% |
| j | 1773 | 2.8% |
| 1572 | 2.5% | |
| technol | 1383 | 2.2% |
| acm | 1083 | 1.7% |
| Other values (3800) | 37917 |
Most occurring characters
| Value | Count | Frequency (%) |
| 51012 | 13.2% | |
| . | 40354 | 10.4% |
| o | 24721 | 6.4% |
| n | 23542 | 6.1% |
| t | 17652 | 4.6% |
| c | 17231 | 4.5% |
| e | 16524 | 4.3% |
| C | 14082 | 3.6% |
| r | 13548 | 3.5% |
| i | 12241 | 3.2% |
| Other values (75) | 156103 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 387010 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 51012 | 13.2% | |
| . | 40354 | 10.4% |
| o | 24721 | 6.4% |
| n | 23542 | 6.1% |
| t | 17652 | 4.6% |
| c | 17231 | 4.5% |
| e | 16524 | 4.3% |
| C | 14082 | 3.6% |
| r | 13548 | 3.5% |
| i | 12241 | 3.2% |
| Other values (75) | 156103 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 387010 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 51012 | 13.2% | |
| . | 40354 | 10.4% |
| o | 24721 | 6.4% |
| n | 23542 | 6.1% |
| t | 17652 | 4.6% |
| c | 17231 | 4.5% |
| e | 16524 | 4.3% |
| C | 14082 | 3.6% |
| r | 13548 | 3.5% |
| i | 12241 | 3.2% |
| Other values (75) | 156103 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 387010 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 51012 | 13.2% | |
| . | 40354 | 10.4% |
| o | 24721 | 6.4% |
| n | 23542 | 6.1% |
| t | 17652 | 4.6% |
| c | 17231 | 4.5% |
| e | 16524 | 4.3% |
| C | 14082 | 3.6% |
| r | 13548 | 3.5% |
| i | 12241 | 3.2% |
| Other values (75) | 156103 |
Document Type
Categorical
| Distinct | 13 |
|---|---|
| Distinct (%) | 0.1% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 720.7 KiB |
| Conference paper | |
|---|---|
| Article | |
| Book chapter | |
| Review | 471 |
| Conference review | 350 |
| Other values (8) | 398 |
Length
| Max length | 17 |
|---|---|
| Median length | 16 |
| Mean length | 11.524524 |
| Min length | 4 |
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | Article |
|---|---|
| 2nd row | Article |
| 3rd row | Article |
| 4th row | Article |
| 5th row | Article |
Common Values
| Value | Count | Frequency (%) |
| Conference paper | 5387 | |
| Article | 4739 | |
| Book chapter | 847 | 6.9% |
| Review | 471 | 3.9% |
| Conference review | 350 | 2.9% |
| Book | 221 | 1.8% |
| Editorial | 54 | 0.4% |
| Note | 49 | 0.4% |
| Erratum | 28 | 0.2% |
| Short survey | 21 | 0.2% |
| Other values (3) | 25 | 0.2% |
Length
| Value | Count | Frequency (%) |
| conference | 5737 | |
| paper | 5389 | |
| article | 4739 | |
| book | 1068 | 5.7% |
| chapter | 847 | 4.5% |
| review | 821 | 4.4% |
| editorial | 54 | 0.3% |
| note | 49 | 0.3% |
| erratum | 28 | 0.1% |
| short | 21 | 0.1% |
| Other values (4) | 46 | 0.2% |
Most occurring characters
| Value | Count | Frequency (%) |
| e | 29944 | |
| r | 17237 | |
| p | 11625 | 8.3% |
| n | 11474 | 8.2% |
| c | 11333 | 8.1% |
| o | 7997 | 5.7% |
| 6607 | 4.7% | |
| a | 6332 | 4.5% |
| t | 5786 | 4.1% |
| C | 5737 | 4.1% |
| Other values (20) | 26435 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 140507 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| e | 29944 | |
| r | 17237 | |
| p | 11625 | 8.3% |
| n | 11474 | 8.2% |
| c | 11333 | 8.1% |
| o | 7997 | 5.7% |
| 6607 | 4.7% | |
| a | 6332 | 4.5% |
| t | 5786 | 4.1% |
| C | 5737 | 4.1% |
| Other values (20) | 26435 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 140507 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| e | 29944 | |
| r | 17237 | |
| p | 11625 | 8.3% |
| n | 11474 | 8.2% |
| c | 11333 | 8.1% |
| o | 7997 | 5.7% |
| 6607 | 4.7% | |
| a | 6332 | 4.5% |
| t | 5786 | 4.1% |
| C | 5737 | 4.1% |
| Other values (20) | 26435 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 140507 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| e | 29944 | |
| r | 17237 | |
| p | 11625 | 8.3% |
| n | 11474 | 8.2% |
| c | 11333 | 8.1% |
| o | 7997 | 5.7% |
| 6607 | 4.7% | |
| a | 6332 | 4.5% |
| t | 5786 | 4.1% |
| C | 5737 | 4.1% |
| Other values (20) | 26435 |
Publication Stage
Categorical
High correlation Imbalance
| Distinct | 2 |
|---|---|
| Distinct (%) | < 0.1% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 642.8 KiB |
| Final | |
|---|---|
| aip | 116 |
Length
| Max length | 5 |
|---|---|
| Median length | 5 |
| Mean length | 4.9809711 |
| Min length | 3 |
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | Final |
|---|---|
| 2nd row | Final |
| 3rd row | Final |
| 4th row | Final |
| 5th row | Final |
Common Values
| Value | Count | Frequency (%) |
| Final | 12076 | |
| aip | 116 | 1.0% |
Length
Common Values (Plot)
| Value | Count | Frequency (%) |
| final | 12076 | |
| aip | 116 | 1.0% |
Most occurring characters
| Value | Count | Frequency (%) |
| i | 12192 | |
| a | 12192 | |
| F | 12076 | |
| n | 12076 | |
| l | 12076 | |
| p | 116 | 0.2% |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 60728 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| i | 12192 | |
| a | 12192 | |
| F | 12076 | |
| n | 12076 | |
| l | 12076 | |
| p | 116 | 0.2% |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 60728 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| i | 12192 | |
| a | 12192 | |
| F | 12076 | |
| n | 12076 | |
| l | 12076 | |
| p | 116 | 0.2% |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 60728 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| i | 12192 | |
| a | 12192 | |
| F | 12076 | |
| n | 12076 | |
| l | 12076 | |
| p | 116 | 0.2% |
Open Access
Categorical
Missing
| Distinct | 20 |
|---|---|
| Distinct (%) | 0.6% |
| Missing | 8657 |
| Missing (%) | 71.0% |
| Memory size | 823.8 KiB |
| All Open Access; Gold Open Access | |
|---|---|
| All Open Access; Gold Open Access; Green Accepted Open Access; Green Open Access | |
| All Open Access; Hybrid Gold Open Access | |
| All Open Access; Green Accepted Open Access; Green Open Access | |
| All Open Access; Bronze Open Access | |
| Other values (15) |
Length
| Max length | 112 |
|---|---|
| Median length | 107 |
| Mean length | 52.461103 |
| Min length | 15 |
Unique
| Unique | 3 ? |
|---|---|
| Unique (%) | 0.1% |
Sample
| 1st row | All Open Access; Hybrid Gold Open Access |
|---|---|
| 2nd row | All Open Access; Hybrid Gold Open Access |
| 3rd row | All Open Access; Hybrid Gold Open Access |
| 4th row | All Open Access; Hybrid Gold Open Access |
| 5th row | All Open Access; Gold Open Access |
Common Values
| Value | Count | Frequency (%) |
| All Open Access; Gold Open Access | 1388 | 11.4% |
| All Open Access; Gold Open Access; Green Accepted Open Access; Green Open Access | 690 | 5.7% |
| All Open Access; Hybrid Gold Open Access | 383 | 3.1% |
| All Open Access; Green Accepted Open Access; Green Open Access | 305 | 2.5% |
| All Open Access; Bronze Open Access | 274 | 2.2% |
| All Open Access; Green Accepted Open Access; Green Open Access; Hybrid Gold Open Access | 177 | 1.5% |
| All Open Access; Gold Open Access; Green Final Open Access; Green Open Access | 76 | 0.6% |
| All Open Access; Bronze Open Access; Green Accepted Open Access; Green Open Access | 76 | 0.6% |
| All Open Access; Green Final Open Access; Green Open Access | 44 | 0.4% |
| All Open Access; Gold Open Access; Green Accepted Open Access; Green Final Open Access; Green Open Access | 35 | 0.3% |
| Other values (10) | 87 | 0.7% |
| (Missing) | 8657 |
Length
| Value | Count | Frequency (%) |
| open | 9718 | |
| access | 9718 | |
| all | 3535 | 11.3% |
| green | 3021 | 9.7% |
| gold | 2791 | 8.9% |
| accepted | 1311 | 4.2% |
| hybrid | 601 | 1.9% |
| bronze | 371 | 1.2% |
| final | 227 | 0.7% |
Most occurring characters
| Value | Count | Frequency (%) |
| e | 28471 | |
| 27758 | ||
| c | 22058 | |
| s | 19436 | |
| A | 14564 | |
| n | 13337 | |
| p | 11029 | 5.9% |
| l | 10088 | 5.4% |
| O | 9718 | 5.2% |
| ; | 6183 | 3.3% |
| Other values (13) | 22808 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 185450 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| e | 28471 | |
| 27758 | ||
| c | 22058 | |
| s | 19436 | |
| A | 14564 | |
| n | 13337 | |
| p | 11029 | 5.9% |
| l | 10088 | 5.4% |
| O | 9718 | 5.2% |
| ; | 6183 | 3.3% |
| Other values (13) | 22808 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 185450 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| e | 28471 | |
| 27758 | ||
| c | 22058 | |
| s | 19436 | |
| A | 14564 | |
| n | 13337 | |
| p | 11029 | 5.9% |
| l | 10088 | 5.4% |
| O | 9718 | 5.2% |
| ; | 6183 | 3.3% |
| Other values (13) | 22808 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 185450 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| e | 28471 | |
| 27758 | ||
| c | 22058 | |
| s | 19436 | |
| A | 14564 | |
| n | 13337 | |
| p | 11029 | 5.9% |
| l | 10088 | 5.4% |
| O | 9718 | 5.2% |
| ; | 6183 | 3.3% |
| Other values (13) | 22808 |
Source
Categorical
Constant
| Distinct | 1 |
|---|---|
| Distinct (%) | < 0.1% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 655.0 KiB |
| Scopus |
|---|
Length
| Max length | 6 |
|---|---|
| Median length | 6 |
| Mean length | 6 |
| Min length | 6 |
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | Scopus |
|---|---|
| 2nd row | Scopus |
| 3rd row | Scopus |
| 4th row | Scopus |
| 5th row | Scopus |
Common Values
| Value | Count | Frequency (%) |
| Scopus | 12192 |
Length
Common Values (Plot)
| Value | Count | Frequency (%) |
| scopus | 12192 |
Most occurring characters
| Value | Count | Frequency (%) |
| S | 12192 | |
| c | 12192 | |
| o | 12192 | |
| p | 12192 | |
| u | 12192 | |
| s | 12192 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 73152 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| S | 12192 | |
| c | 12192 | |
| o | 12192 | |
| p | 12192 | |
| u | 12192 | |
| s | 12192 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 73152 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| S | 12192 | |
| c | 12192 | |
| o | 12192 | |
| p | 12192 | |
| u | 12192 | |
| s | 12192 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 73152 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| S | 12192 | |
| c | 12192 | |
| o | 12192 | |
| p | 12192 | |
| u | 12192 | |
| s | 12192 |
EID
Text
Unique
| Distinct | 12192 |
|---|---|
| Distinct (%) | 100.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 798.9 KiB |
Length
| Max length | 19 |
|---|---|
| Median length | 18 |
| Mean length | 18.088583 |
| Min length | 17 |
Unique
| Unique | 12192 ? |
|---|---|
| Unique (%) | 100.0% |
Sample
| 1st row | 2-s2.0-105023692746 |
|---|---|
| 2nd row | 2-s2.0-105022798679 |
| 3rd row | 2-s2.0-105021925885 |
| 4th row | 2-s2.0-105021238108 |
| 5th row | 2-s2.0-105025196185 |
| Value | Count | Frequency (%) |
| 2-s2.0-0021706671 | 1 | < 0.1% |
| 2-s2.0-0021700041 | 1 | < 0.1% |
| 2-s2.0-0021539713 | 1 | < 0.1% |
| 2-s2.0-0021526351 | 1 | < 0.1% |
| 2-s2.0-0021497059 | 1 | < 0.1% |
| 2-s2.0-85119502987 | 1 | < 0.1% |
| 2-s2.0-67650426074 | 1 | < 0.1% |
| 2-s2.0-2842594331 | 1 | < 0.1% |
| 2-s2.0-0020837797 | 1 | < 0.1% |
| 2-s2.0-0020804776 | 1 | < 0.1% |
| Other values (12182) | 12182 |
Most occurring characters
| Value | Count | Frequency (%) |
| 2 | 35201 | |
| 0 | 29172 | |
| - | 24384 | |
| 8 | 20166 | |
| 5 | 19327 | |
| 1 | 15988 | |
| s | 12192 | 5.5% |
| . | 12192 | 5.5% |
| 4 | 11535 | 5.2% |
| 9 | 11111 | 5.0% |
| Other values (3) | 29268 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 220536 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 2 | 35201 | |
| 0 | 29172 | |
| - | 24384 | |
| 8 | 20166 | |
| 5 | 19327 | |
| 1 | 15988 | |
| s | 12192 | 5.5% |
| . | 12192 | 5.5% |
| 4 | 11535 | 5.2% |
| 9 | 11111 | 5.0% |
| Other values (3) | 29268 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 220536 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 2 | 35201 | |
| 0 | 29172 | |
| - | 24384 | |
| 8 | 20166 | |
| 5 | 19327 | |
| 1 | 15988 | |
| s | 12192 | 5.5% |
| . | 12192 | 5.5% |
| 4 | 11535 | 5.2% |
| 9 | 11111 | 5.0% |
| Other values (3) | 29268 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 220536 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 2 | 35201 | |
| 0 | 29172 | |
| - | 24384 | |
| 8 | 20166 | |
| 5 | 19327 | |
| 1 | 15988 | |
| s | 12192 | 5.5% |
| . | 12192 | 5.5% |
| 4 | 11535 | 5.2% |
| 9 | 11111 | 5.0% |
| Other values (3) | 29268 |
doi_norm
Text
Missing
| Distinct | 10106 |
|---|---|
| Distinct (%) | 99.8% |
| Missing | 2068 |
| Missing (%) | 17.0% |
| Memory size | 801.5 KiB |
Length
| Max length | 66 |
|---|---|
| Median length | 58 |
| Mean length | 25.522916 |
| Min length | 12 |
Unique
| Unique | 10088 ? |
|---|---|
| Unique (%) | 99.6% |
Sample
| 1st row | 10.1016/j.tsc.2025.102068 |
|---|---|
| 2nd row | 10.1016/j.tsc.2025.102070 |
| 3rd row | 10.1016/j.tsc.2025.102056 |
| 4th row | 10.1016/j.tsc.2025.102049 |
| 5th row | 10.1016/j.neunet.2025.108407 |
| Value | Count | Frequency (%) |
| 10.1007/s11423-023-10328-8 | 2 | < 0.1% |
| 10.1051/e3sconf/202453805034 | 2 | < 0.1% |
| 10.1145/1140124.1140161 | 2 | < 0.1% |
| 10.1016/j.procir.2024.10.161 | 2 | < 0.1% |
| 10.1145/3159450.3159586 | 2 | < 0.1% |
| 10.1016/b978-0-12-809324-5.23765-6 | 2 | < 0.1% |
| 10.1016/b978-044451719-7/50072-x | 2 | < 0.1% |
| 10.1016/b978-0-12-804071-3.00012-4 | 2 | < 0.1% |
| 10.4324/9781351232357 | 2 | < 0.1% |
| 10.34190/gbl.20.156 | 2 | < 0.1% |
| Other values (10096) | 10104 |
Most occurring characters
| Value | Count | Frequency (%) |
| 1 | 41207 | |
| 0 | 40154 | |
| . | 22455 | 8.7% |
| 2 | 18979 | 7.3% |
| 3 | 15621 | 6.0% |
| 9 | 13345 | 5.2% |
| 7 | 12030 | 4.7% |
| 4 | 11597 | 4.5% |
| 5 | 11450 | 4.4% |
| 8 | 11024 | 4.3% |
| Other values (38) | 60532 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 258394 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 1 | 41207 | |
| 0 | 40154 | |
| . | 22455 | 8.7% |
| 2 | 18979 | 7.3% |
| 3 | 15621 | 6.0% |
| 9 | 13345 | 5.2% |
| 7 | 12030 | 4.7% |
| 4 | 11597 | 4.5% |
| 5 | 11450 | 4.4% |
| 8 | 11024 | 4.3% |
| Other values (38) | 60532 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 258394 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 1 | 41207 | |
| 0 | 40154 | |
| . | 22455 | 8.7% |
| 2 | 18979 | 7.3% |
| 3 | 15621 | 6.0% |
| 9 | 13345 | 5.2% |
| 7 | 12030 | 4.7% |
| 4 | 11597 | 4.5% |
| 5 | 11450 | 4.4% |
| 8 | 11024 | 4.3% |
| Other values (38) | 60532 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 258394 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 1 | 41207 | |
| 0 | 40154 | |
| . | 22455 | 8.7% |
| 2 | 18979 | 7.3% |
| 3 | 15621 | 6.0% |
| 9 | 13345 | 5.2% |
| 7 | 12030 | 4.7% |
| 4 | 11597 | 4.5% |
| 5 | 11450 | 4.4% |
| 8 | 11024 | 4.3% |
| Other values (38) | 60532 |
Interactions
Correlations
| Cited by | Conference code | Document Type | Language of Original Document | Open Access | PubMed ID | Publication Stage | Year | |
|---|---|---|---|---|---|---|---|---|
| Cited by | 1.000 | -0.418 | 0.035 | 0.000 | 0.056 | -0.457 | 0.000 | -0.360 |
| Conference code | -0.418 | 1.000 | 0.041 | 0.094 | 0.177 | 1.000 | 1.000 | 0.727 |
| Document Type | 0.035 | 0.041 | 1.000 | 0.021 | 0.118 | 0.050 | 0.113 | 0.066 |
| Language of Original Document | 0.000 | 0.094 | 0.021 | 1.000 | 0.000 | 0.251 | 0.000 | 0.027 |
| Open Access | 0.056 | 0.177 | 0.118 | 0.000 | 1.000 | 0.156 | 0.141 | 0.140 |
| PubMed ID | -0.457 | 1.000 | 0.050 | 0.251 | 0.156 | 1.000 | 0.041 | 0.996 |
| Publication Stage | 0.000 | 1.000 | 0.113 | 0.000 | 0.141 | 0.041 | 1.000 | 0.087 |
| Year | -0.360 | 0.727 | 0.066 | 0.027 | 0.140 | 0.996 | 0.087 | 1.000 |
Missing values
Sample
| Authors | Author full names | Author(s) ID | Title | Year | Source title | Volume | Issue | Art. No. | Page start | Page end | Cited by | DOI | Link | Affiliations | Authors with affiliations | Abstract | Author Keywords | Index Keywords | Molecular Sequence Numbers | Chemicals/CAS | Tradenames | Manufacturers | Funding Details | Funding Texts | References | Correspondence Address | Editors | Publisher | Sponsors | Conference name | Conference date | Conference location | Conference code | ISSN | ISBN | CODEN | PubMed ID | Language of Original Document | Abbreviated Source Title | Document Type | Publication Stage | Open Access | Source | EID | doi_norm | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Wang, Y. | Wang, Yang (57208730125) | 57208730125 | Effects of troubleshooting robotics learning on students’ engagement, computational thinking, and programming skills | 2026 | Thinking Skills and Creativity | 60 | NaN | 102068 | NaN | NaN | 0 | 10.1016/j.tsc.2025.102068 | https://www.scopus.com/inward/record.uri?eid=2-s2.0-105023692746&doi=10.1016%2Fj.tsc.2025.102068&partnerID=40&md5=a8d0b7d575d39357a9ab8fabecdc6c4e | Nanjing Normal University, Nanjing, Jiangsu, China | Wang, Yang, Nanjing Normal University, Nanjing, Jiangsu, China | Learning engagement is an important indicator of active learning outcomes. Computational thinking is a basic competency required in the 21st century. Troubleshooting learning is helpful to enhance students’ computational thinking and engagement, as its targeted error analysis addresses traditional learning’s limitation of insufficient guidance on error-prone points. However, the role of troubleshooting in students’ engagement and computational thinking in robotics programming learning is to be explored. To fill in this gap, the current study explored the effects of troubleshooting robotics programming learning on students’ engagement, computational thinking, and programming skills. A quasi-experimental study was conducted to explore the effects of troubleshooting learning on students’ robotics programming learning by comparing students’ learning results in two courses instructed by the same instructor (one instructed with a problem-based method, the other instructed with a troubleshooting method). The participants were seventy-nine students from a university in China. Questionnaires, tests, and work analyses were used to measure students’ engagement, computational thinking, and programming skills. The results indicated that troubleshooting learning is more effective in enhancing students’ engagement (i.e., behavioral, cognitive, and emotional engagement), computational thinking (i.e., cooperativity, critical thinking, and creativity) and programming learning (i.e., data representation). The findings provide insight into troubleshooting-supported robotics programming learning. Different types of troubleshooting tasks with progressive difficulty are effective in enhancing students’ learning. Troubleshooting could be used in the early stages of programming learning to help students master the error prone areas of programming. © 2025 Elsevier Ltd. | Computational thinking; Programming skills; Robotics programming learning; Troubleshooting | NaN | NaN | NaN | NaN | NaN | Ministry of Education, MOE; Major Project of Philosophy and Social Science Research in Colleges and Universities of Jiangsu Province, (25JYC004) | This work was supported by the Project of Humanities and Social Sciences Program of the Ministry of Education , the Philosophy and Social Science Research project of Jiangsu province (No. 25JYC004 ). | Astin, Alexander W., Student involvement: A developmental theory for higher education, Journal of College Student Development, 40, 5, pp. 518-529, (1999); Atmatzidou, Soumela, Advancing students' computational thinking skills through educational robotics: A study on age and gender relevant differences, Robotics and Autonomous Systems, 75, pp. 661-670, (2016); Bacca, Jorge, Student engagement with mobile-based assessment systems: A survival analysis, Journal of Computer Assisted Learning, 37, 1, pp. 158-171, (2021); Melander Bowden, Helen, Problem-solving in collaborative game design practices: epistemic stance, affect, and engagement, Learning, Media and Technology, 44, 2, pp. 124-143, (2019); APA Handbook of Research Methods in Psychology Research Designs Quantitative Qualitative Neuropsychological and Biological, (2023); Buil, Isabel, Engagement in business simulation games: A self-system model of motivational development, British Journal of Educational Technology, 51, 1, pp. 297-311, (2020); Çakır, Recep, The effect of robotic coding education on preschoolers’ problem solving and creative thinking skills, Thinking Skills and Creativity, 40, (2021); Thinking Skills and Creativity, (2021); Chao, Poyao, Exploring students' computational practice, design and performance of problem-solving through a visual programming environment, Computers and Education, 95, pp. 202-215, (2016); undefined | Y. Wang; Adolescent Education and Intelligence Support Lab of Nanjing Normal University, Nanjing, China; email: wangyang@nnu.edu.cn | NaN | Elsevier Ltd | NaN | NaN | NaN | NaN | NaN | 18711871 | NaN | NaN | NaN | English | Think. Skills Creat. | Article | Final | NaN | Scopus | 2-s2.0-105023692746 | 10.1016/j.tsc.2025.102068 |
| 1 | Lin, Y.; Zhang, Y.; Yang, Y.; Pan, S.; Ren, X.; Chen, D. | Lin, Yuru (57281795200); Zhang, Yi (58957195500); Yang, Yuqin (57164390600); Pan, Shidan (60209651800); Ren, Xu (60209651900); Chen, Dengkang (57898076100) | 57281795200; 58957195500; 57164390600; 60209651800; 60209651900; 57898076100 | Facilitating computational thinking with AI: A three-level meta-analytic evidence for future-ready learning | 2026 | Thinking Skills and Creativity | 60 | NaN | 102070 | NaN | NaN | 0 | 10.1016/j.tsc.2025.102070 | https://www.scopus.com/inward/record.uri?eid=2-s2.0-105022798679&doi=10.1016%2Fj.tsc.2025.102070&partnerID=40&md5=420bda068ce007f4339494c4fa7783c7 | Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, China | Lin, Yuru, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, China; Zhang, Yi, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, China; Yang, Yuqin, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, China; Pan, Shidan, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, China; Ren, Xu, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, China; Chen, Dengkang, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, China | The integration of artificial intelligence (AI) tools in education to promote computational thinking (CT) among students has become a trending topic of research; however, there is no consensus on the impact of such tools on CT. Qualitative syntheses regarding both the effect of AI tools and how to unleash their power more effectively are also lacking. Using a three-level meta-analytic approach, this study evaluated the effectiveness of AI tools in improving students’ CT and investigated the various moderating variables. A total of 32 empirical studies with 44 effect sizes were included in this meta-analysis, and the results showed that AI tools have a significant and moderately large effect on students’ CT (Hedges’s g = 0.75, 95 % CI [0.55, 0.95], p < 0.0001). Moderator analyses revealed that AI technologies, the application of AI tools, as well as tool customization and its method, and sample size significantly influence the effectiveness of AI tools. Other moderators—including region, publication year, subject disciplines, instructional approach, collaboration type, intervention duration, gender, and educational level—appeared to be universally effective in promoting student CT. Overall, this meta-analysis contributes to both the academic understanding and practical application of AI tools in CT education to help students prepare for the smart society of the future. © 2025 Elsevier Ltd. | Artificial intelligence; Artificial intelligence in education; Computational thinking; Moderator analysis; Three-level meta-analysis | NaN | NaN | NaN | NaN | NaN | National Natural Science Foundation of China, NSFC, (72274076); Fundamental Research Funds for the Central Universities, (30106250032) | This study was funded by the 2023 National Natural Science Foundation of China (Grant No. 72274076) and funded by the Fundamental Research Funds for the Central Universities (Outstanding Innovation Project, No. 30106250032). | Aldabe, Itziar, Semantic similarity measures for the generation of science tests in basque, IEEE Transactions on Learning Technologies, 7, 4, pp. 375-387, (2014); Ameen, Linda Talib, The Impact of Artificial Intelligence on Computational Thinking in Education at University, International Journal of Engineering Pedagogy, 14, 5, pp. 192-203, (2024); Angeli Valanides, Charoula Nicos, Investigating the effects of gender and scaffolding in developing preschool children’s computational thinking during problem-solving with Bee-Bots, Frontiers in Education, 7, (2023); Asunda, Paul A., Embracing Computational Thinking as an Impetus for Artificial Intelligence in Integrated STEM Disciplines through Engineering and Technology Education, Journal of Technology Education, 34, 2, pp. 43-63, (2023); Atkinson, Richard C., Human Memory: A Proposed System and its Control Processes, Psychology of Learning and Motivation - Advances in Research and Theory, 2, C, pp. 89-195, (1968); Jbi Manual for Evidence Synthesis, (2024); Educ AI Tion Rebooted Exploring the Future of Artificial Intelligence in Schools and Colleges, (2019); Basu, Satabdi, Learner modeling for adaptive scaffolding in a Computational Thinking-based science learning environment, User Modeling and User-Adapted Interaction, 27, 1, pp. 5-53, (2017); Bhatt, Sohum Mandar, A Method for Developing Process-Based Assessments for Computational Thinking Tasks, Journal of Learning Analytics, 11, 2, pp. 157-173, (2024); Belland, Brian R., A Bayesian Network Meta-Analysis to Synthesize the Influence of Contexts of Scaffolding Use on Cognitive Outcomes in STEM Education, Review of Educational Research, 87, 6, pp. 1042-1081, (2017) | Y. Yang; Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, No. 152 Luoyu Road, Hubei, 430079, China; email: yangyuqin@ccnu.edu.cn | NaN | Elsevier Ltd | NaN | NaN | NaN | NaN | NaN | 18711871 | NaN | NaN | NaN | English | Think. Skills Creat. | Article | Final | NaN | Scopus | 2-s2.0-105022798679 | 10.1016/j.tsc.2025.102070 |
| 2 | Hsu, T.-C.; Hsu, T.-P. | Hsu, Tingchia (35173046500); Hsu, Taiping (58366049000) | 35173046500; 58366049000 | Effects of game-based learning integrated with different thinking-guided methods on computational thinking of elementary school students | 2026 | Thinking Skills and Creativity | 60 | NaN | 102056 | NaN | NaN | 0 | 10.1016/j.tsc.2025.102056 | https://www.scopus.com/inward/record.uri?eid=2-s2.0-105021925885&doi=10.1016%2Fj.tsc.2025.102056&partnerID=40&md5=11dec6c981e61a08f0343592d5fbed9f | Department of Technology Application and Human Resource Development, National Taiwan Normal University, Taipei, Taiwan | Hsu, Tingchia, Department of Technology Application and Human Resource Development, National Taiwan Normal University, Taipei, Taiwan; Hsu, Taiping, Department of Technology Application and Human Resource Development, National Taiwan Normal University, Taipei, Taiwan | The study developed an online game system for young students to learn computational thinking (CT), and explored the CT learning achievements and self-efficacy of students using two thinking-guided methods. One method was 5W1H, which is well known in science learning, and the other was concept-association-based concept mapping (CABCM). These thinking-guided methods, aimed at the beginning stage of problem analysis, were utilized before playing the online game, with the aim of helping students learn and solve CT tasks in the game scenarios. The research involved 54 students whose average age was 10, divided into two groups based on the different thinking-guided methods. The experimental results showed that students in both the CABCM and 5W1H groups demonstrated significant learning gains in CT achievement and self-efficacy from pre-test to post-test. While no statistically significant difference was found in the post-test scores between the two groups, a detailed analysis of learning behaviors revealed distinct problem-solving pathways associated with each thinking-guided method. The findings suggest that both integrated approaches effectively fostered CT skills, albeit through different cognitive processes. This research contributes to CT education by integrating thinking-guided methods into an online CT game. It offers empirical evidence on the effectiveness of such integrated approaches and provides insights into the processes and behaviors associated with different thinking-guided methods, shedding light on students' challenges in learning CT through games. © © 2025. Published by Elsevier Ltd. | 5W1H; Computational thinking; Concept-association-based concept mapping strategy; Self-efficacy | NaN | NaN | NaN | NaN | NaN | (NSTC 111-2410-H-003-168-MY3) | This study is supported in part by the National Science and Technology Council in the Republic of China under contract numbers NSTC 111-2410-H-003-168-MY3 . | Alsadoon, Elham, Effects of a gamified learning environment on students’ achievement, motivations, and satisfaction, Heliyon, 8, 8, (2022); Journal of Languages and Language Teaching, (2023); Avcı, Canan, Computational thinking: early childhood teachers’ and prospective teachers’ preconceptions and self-efficacy, Education and Information Technologies, 27, 8, pp. 11689-11713, (2022); Bakeman, Roger A., Observer agreement for timed-event sequential data: A comparison of time-based and event-based algorithms, Behavior Research Methods, 41, 1, pp. 137-147, (2009); Bers, Marina Umaschi, Computational thinking and tinkering: Exploration of an early childhood robotics curriculum, Computers and Education, 72, pp. 145-157, (2014); Annual American Educational Research Association Meeting, (2012); Chao, Poyao, Exploring students' computational practice, design and performance of problem-solving through a visual programming environment, Computers and Education, 95, pp. 202-215, (2016); Cheng, Shuchen, Facilitating creativity, collaboration, and computational thinking in group website design: a concept mapping-based mobile flipped learning approach, International Journal of Mobile Learning and Organisation, 18, 2, pp. 169-193, (2024); Cheng, Yuping, Enhancing student's computational thinking skills with student-generated questions strategy in a game-based learning platform, Computers and Education, 200, (2023); Chevalier, Morgane, The role of feedback and guidance as intervention methods to foster computational thinking in educational robotics learning activities for primary school, Computers and Education, 180, (2022) | T.-P. Hsu; Department of Technology Application and Human Resource Development, National Taiwan Normal University, Taipei city, 162, Sec. 1, East Heping Rd, 10610, Taiwan; email: 81171002H@ntnu.edu.tw | NaN | Elsevier Ltd | NaN | NaN | NaN | NaN | NaN | 18711871 | NaN | NaN | NaN | English | Think. Skills Creat. | Article | Final | NaN | Scopus | 2-s2.0-105021925885 | 10.1016/j.tsc.2025.102056 |
| 3 | Aksoy, B.D.; Mumcu, F.K.; Cantürk Günhan, B.C. | Aksoy, Behiye Dinçer (60177502400); Mumcu, Filiz Kuşkaya (13410584100); Cantürk Günhan, Berna (36815607700) | 60177502400; 13410584100; 36815607700 | Unveiling the nexus: Computational thinking and mathematical modelling in K-12 education- a teacher-centric exploration | 2026 | Thinking Skills and Creativity | 60 | NaN | 102049 | NaN | NaN | 0 | 10.1016/j.tsc.2025.102049 | https://www.scopus.com/inward/record.uri?eid=2-s2.0-105021238108&doi=10.1016%2Fj.tsc.2025.102049&partnerID=40&md5=6a9d8142edba11229cdd741ebbab5ecd | General Directorate of Innovation and Educational Technologies, Ankara, Turkey; Department of Early Childhood Education, Universität Graz, Graz, Styria, Austria; Department of Mathematics and Science Education, Dokuz Eylül Üniversitesi, Izmir, Turkey | Aksoy, Behiye Dinçer, General Directorate of Innovation and Educational Technologies, Ankara, Turkey; Mumcu, Filiz Kuşkaya, Department of Early Childhood Education, Universität Graz, Graz, Styria, Austria; Cantürk Günhan, Berna, Department of Mathematics and Science Education, Dokuz Eylül Üniversitesi, Izmir, Turkey | This study explores how Computational Thinking (CT) components overlap with the phases of mathematical modelling within the context of a Teacher Development Course (TDC). The course was designed, developed, implemented, and assessed to enhance teachers’ cognitive actions in integrating CT with mathematical modelling. This research study was conducted with three mathematics teachers and one computer science teacher. Data were collected through CT component worksheets and video recordings, and analysed based on Borromeo-Ferri’s (2006) modelling cycle and the study’s CT framework. The study’s findings indicate that modelling processes enhanced teachers’ CT skills, while CT components made the modelling process more structured and reflective, revealing a reciprocal relationship between modelling and CT. The study proposes an original interdisciplinary framework linking teachers’ cognitive actions to CT integration, offering both theoretical and practical contributions. © 2025 The Author(s). | Computational thinking; CT components; CT-integrated maths education; Mathematical modelling; Teacher development | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Turkish Studies Educational Sciences, (2020); Mathematical Modelling Education in East and West, (2021); Journal of Theory and Practice in Education, (2017); Barr, Valerie B., Bringing computational thinking to K-12: What is involved and what is the role of the computer science education community?, ACM Inroads, 2, 1, pp. 48-54, (2011); Mathematical Epistemology and Psychology, (1966); Journal of Mathematical Modelling and Application, (2009); Modelling and Applications in Mathematics Education, (2007); Mathematical Modelling Ictma 12 Education Engineering and Economics, (2007); Modelling Applications and Applied Problem Solving, (1989); Borromeo-Ferri, Rita, Theoretical and empirical differentiations of phases in the modelling process, ZDM - International Journal on Mathematics Education, 38, 2, pp. 86-95, (2006) | F.K. Mumcu; Digitalization in Early Childhood Education, Department of Education Research and Teacher Education, University of Graz, Graz, Austria; email: filiz.mumcu@uni-graz.at | NaN | Elsevier Ltd | NaN | NaN | NaN | NaN | NaN | 18711871 | NaN | NaN | NaN | English | Think. Skills Creat. | Article | Final | All Open Access; Hybrid Gold Open Access | Scopus | 2-s2.0-105021238108 | 10.1016/j.tsc.2025.102049 |
| 4 | van Bergen, R.; Huebotter, J.; A.; Lanillos, P. | van Bergen, Ruben S. (55502596000); Huebotter, Justus F. (57901993200); Lanillos, Pablo (24076529300) | 55502596000; 57901993200; 60247114700; 24076529300 | Object-centric proto-symbolic behavioural reasoning from pixels | 2026 | Neural Networks | 197 | NaN | 108407 | NaN | NaN | 0 | 10.1016/j.neunet.2025.108407 | https://www.scopus.com/inward/record.uri?eid=2-s2.0-105025196185&doi=10.1016%2Fj.neunet.2025.108407&partnerID=40&md5=1371a88426221f67f7792f02362b7a13 | Radboud Universiteit, Nijmegen, Gelderland, Netherlands; Cajal International Center for Neuroscience, Consejo Superior de Investigaciones Científicas, Madrid, Madrid, Spain | van Bergen, Ruben S., Radboud Universiteit, Nijmegen, Gelderland, Netherlands; Huebotter, Justus F., Radboud Universiteit, Nijmegen, Gelderland, Netherlands; null, null, Cajal International Center for Neuroscience, Consejo Superior de Investigaciones Científicas, Madrid, Madrid, Spain; Lanillos, Pablo, Radboud Universiteit, Nijmegen, Gelderland, Netherlands, Cajal International Center for Neuroscience, Consejo Superior de Investigaciones Científicas, Madrid, Madrid, Spain | Autonomous intelligent agents must bridge computational challenges at disparate levels of abstraction, from the low-level spaces of sensory input and motor commands to the high-level domain of abstract reasoning and planning. A key question in designing such agents is how best to instantiate the representational space that will interface between these two levels—ideally without requiring supervision in the form of expensive data annotations. These objectives can be efficiently achieved by representing the world in terms of objects (grounded in perception and action). In this work, we present a novel, brain-inspired, deep-learning architecture that learns from pixels to interpret, control, and reason about its environment, using object-centric representations. We show the utility of our approach through tasks in synthetic environments that require a combination of (high-level) logical reasoning and (low-level) continuous control. Results show that the agent can learn emergent conditional behavioural reasoning, such as (A → B)∧(¬A → C), as well as logical composition (A → B)∧(A → C)⊢A → (B∧C) and XOR operations, and successfully controls its environment to satisfy objectives deduced from these logical rules. The agent can adapt online to unexpected changes in its environment and is robust to mild violations of its world model, thanks to dynamic internal desired goal generation. While the present results are limited to synthetic settings (2D and 3D activated versions of dSprites), which fall short of real-world levels of complexity, the proposed architecture shows how to manipulate grounded object representations, as a key inductive bias for unsupervised learning, to enable behavioral reasoning. © 2025 The Author(s) | Brain-inspired perception and control; Deep learning architectures; Object-centric reasoning | Abstracting; Architecture; Behavioral research; Deep learning; Intelligent agents; Memory architecture; Unsupervised learning; Autonomous Intelligent Agents; Behavioral reasoning; Brain-inspired; Brain-inspired perception and control; Computational challenges; Deep learning architecture; Learn+; Learning architectures; Object-centric reasoning; Sensory motors; Autonomous agents; abstract thinking; article; clinical article; controlled study; deep learning; human; learning; logical reasoning; reasoning; sensory stimulation | NaN | NaN | NaN | NaN | NaN | NaN | undefined, (2022); Iclr2022 Workshop on the Elements of Reasoning Objects Structure and Causality, (2022); undefined, (2025); Battaglia, Peter W., Interaction networks for learning about objects, relations and physics, Advances in Neural Information Processing Systems, pp. 4509-4517, (2016); Battaglia, Peter W., Simulation as an engine of physical scene understanding, Proceedings of the National Academy of Sciences of the United States of America, 110, 45, pp. 18327-18332, (2013); van Bergen, Ruben S., Object-Based Active Inference, Communications in Computer and Information Science, 1721 CCIS, pp. 50-64, (2023); Bas, Fred, Free Energy Principle for State and Input Estimation of a Quadcopter Flying in Wind, Proceedings - IEEE International Conference on Robotics and Automation, 2022-January, pp. 5389-5395, (2022); Cowley, Stephen John, How human infants deal with symbol grounding, Interaction Studies, 8, 1, pp. 83-104, (2007); undefined, (2022); Driess, Danny, Learning Multi-Object Dynamics with Compositional Neural Radiance Fields, Proceedings of Machine Learning Research, 205, pp. 1755-1768, (2023) | P. Lanillos; Donders Institute, Radboud University, Nijmegen, Netherlands; email: p.lanillos@csic.es | NaN | Elsevier Ltd | NaN | NaN | NaN | NaN | NaN | 08936080 | NaN | NNETE | NaN | English | Neural Netw. | Article | Final | All Open Access; Hybrid Gold Open Access | Scopus | 2-s2.0-105025196185 | 10.1016/j.neunet.2025.108407 |
| 5 | Hristov, M.; Yada, T.; Fagerlund, J.; Näykki, P.; Häkkinen, P. | Hristov, Mitcho (60244741200); Yada, Takumi (57211492229); Fagerlund, Janne (57204184281); Näykki, Piia (24344723200); Häkkinen, Päivi H. (55917698700) | 60244741200; 57211492229; 57204184281; 24344723200; 55917698700 | Understanding the relationships among ICT use, self-efficacy, and achievement in PISA 2022: A multigroup analysis featuring gender and immigrant status | 2026 | Computers and Education | 244 | NaN | 105539 | NaN | NaN | 0 | 10.1016/j.compedu.2025.105539 | https://www.scopus.com/inward/record.uri?eid=2-s2.0-105025142999&doi=10.1016%2Fj.compedu.2025.105539&partnerID=40&md5=aababda657dab00c082b3c70c04fd383 | University of Jyväskylä, Jyvaskyla, Central Finland, Finland; Faculty of Education and Psychology, University of Jyväskylä, Jyvaskyla, Central Finland, Finland; Department of Teacher Education, University of Jyväskylä, Jyvaskyla, Central Finland, Finland | Hristov, Mitcho, University of Jyväskylä, Jyvaskyla, Central Finland, Finland; Yada, Takumi, University of Jyväskylä, Jyvaskyla, Central Finland, Finland, Faculty of Education and Psychology, University of Jyväskylä, Jyvaskyla, Central Finland, Finland; Fagerlund, Janne, Department of Teacher Education, University of Jyväskylä, Jyvaskyla, Central Finland, Finland; Näykki, Piia, Department of Teacher Education, University of Jyväskylä, Jyvaskyla, Central Finland, Finland; Häkkinen, Päivi H., University of Jyväskylä, Jyvaskyla, Central Finland, Finland | This study investigates the relationships among students’ use of ICT for learning and leisure, self-efficacy in digital competencies, and achievement in math, reading, and science, and compares differences based on gender and immigrant background. Previous studies show inconsistent relationships among these variables. Student background affects the use of ICT, and while self-efficacy may vary depending on the subject, it has had positive effects on academic achievement. In this study, self-efficacy in digital competencies is viewed as two different competence beliefs: computer and information literacy (CIL) and computational thinking (CT). Although self-efficacy in CIL and CT has had varying effects on digital skills, the effects on math, reading, and science are not well studied. We analyzed Finnish data (N = 10,239) from PISA 2022 using general and multigroup structural equation modelling. We found that ICT use for learning had little to no practical significance across groups. ICT use for leisure and self-efficacy in CT were associated with being approximately a year or more behind in math, reading, and science across groups. Self-efficacy in CIL was associated with being two or more years ahead and played a protective short-term role as a mediator, especially in immigrants. These findings imply that closer integration of ICT use for learning with subject-specific goals in authentic learning contexts may promote their contribution to achievement. Further research should examine how different uses of ICT and CT skill development interact with subject learning in practice and how schools can be supported in adopting more pedagogically purposeful digital activities. © 2025 The Authors | Academic achievement; Computational thinking; Computer and information literacy; Digital competencies; ICT use; Mediation effects; Self-efficacy | Computational methods; E-learning; Teaching; Academic achievements; Computational thinkings; Computer literacy; Digital competency; Digital skills; ICT use; Information literacy; Mediation effect; Multi-group; Self efficacy; Students | NaN | NaN | NaN | NaN | NaN | NaN | Bandura, Albert, Perceived Self-Efficacy in Cognitive Development and Functioning, Educational Psychologist, 28, 2, pp. 117-148, (1993); Bhutoria, Aditi, Patterns of cognitive returns to Information and Communication Technology (ICT) use of 15-year-olds: Global evidence from a Hierarchical Linear Modeling approach using PISA 2018, Computers and Education, 181, (2022); Caeli, Elisa Nadire, ICT Use, Self-efficacy, and the Future of Eighth-Grade Students: a Qualitative Study of Gender Differences, TechTrends, 69, 1, pp. 233-243, (2025); Campos, Diego G., Digital gender gaps in Students’ knowledge, attitudes and skills: an integrative data analysis across 32 Countries, Education and Information Technologies, 29, 1, pp. 655-693, (2024); Chen, Fangfang, Sensitivity of goodness of fit indexes to lack of measurement invariance, Structural Equation Modeling, 14, 3, pp. 464-504, (2007); Courtney, Matthew G.R., The influence of ict use and related attitudes on students’ math and science performance: multilevel analyses of the last decade’s pisa surveys, Large-Scale Assessments in Education, 10, 1, (2022); 2nd Survey of Schools ICT in Education Objective 1 Benchmark Progress in ICT in Schools, (2019); Faber, Janke M., The effects of a digital formative assessment tool on mathematics achievement and student motivation: Results of a randomized experiment, Computers and Education, 106, pp. 83-96, (2017); Koulutuksen Tutkimuslaitos Finnish Institute for Educational Research, (2024) | M. Hristov; Finnish Institute for Educational Research, University of Jyväskylä, Jyväskylä, Finland; email: mitcho.a.hristov@jyu.fi | NaN | Elsevier Ltd | NaN | NaN | NaN | NaN | NaN | 03601315 | NaN | COMED | NaN | English | Comput Educ | Article | Final | All Open Access; Hybrid Gold Open Access | Scopus | 2-s2.0-105025142999 | 10.1016/j.compedu.2025.105539 |
| 6 | Kurikawa, T.; Kaneko, K. | Kurikawa, Tomoki (36677377200); Kaneko, Kunihiko (7403696146) | 36677377200; 7403696146 | Stability control of metastable states as a unified mechanism for flexible temporal modulation in cognitive processing | 2026 | Neural Networks | 196 | NaN | 108381 | NaN | NaN | 0 | 10.1016/j.neunet.2025.108381 | https://www.scopus.com/inward/record.uri?eid=2-s2.0-105024706206&doi=10.1016%2Fj.neunet.2025.108381&partnerID=40&md5=cf1a6d80c6651eb4019a383a29b31aaa | Department of Complex and Intelligent Systems, Future University - Hakodate, Hakodate, Hokkaido, Japan; Niels Bohr Institutet, Copenhagen, Hovedstaden, Denmark; Research Center for Complex Systems Biology, The University of Tokyo, Tokyo, Japan | Kurikawa, Tomoki, Department of Complex and Intelligent Systems, Future University - Hakodate, Hakodate, Hokkaido, Japan; Kaneko, Kunihiko, Niels Bohr Institutet, Copenhagen, Hovedstaden, Denmark, Research Center for Complex Systems Biology, The University of Tokyo, Tokyo, Japan | Flexible modulation of temporal dynamics in neural sequences underlies many cognitive processes. For instance, we can adaptively change the speed of motor sequences and speech. While such flexibility is influenced by various factors such as attention and context, the common neural mechanisms responsible for this modulation remain poorly understood. We developed a biologically plausible neural network model that incorporates neurons with multiple timescales and Hebbian learning rules. This model is capable of generating simple sequential patterns as well as performing delayed match-to-sample (DMS) tasks that require the retention of stimulus identity. Fast neural dynamics establish metastable states, while slow neural dynamics maintain task-relevant information and modulate the stability of these states to enable temporal processing. We systematically analyzed how factors such as neuronal gain, external input strength (contextual cues), and task difficulty influence the temporal properties of neural activity sequences-specifically, dwell time within patterns and transition times between successive patterns. We found that these factors flexibly modulate the stability of metastable states. Our findings provide a unified mechanism for understanding various forms of temporal modulation and suggest a novel computational role for neural timescale diversity in dynamically adapting cognitive performance to changing environmental demands. Author Summary: The brain often uses sequences of neural activity to perform complex cognitive tasks such as recognizing speech, making decisions, or holding information in working memory. These sequences can speed up or slow down depending on factors like attention, task difficulty, or expectations-but how the brain controls this timing remains unclear. In this study, we built a biologically plausible model of a neural network that includes both fast and slow neurons and learns tasks through simple, realistic rules. We show that the slow neurons can hold onto past information and control how long the network activity stays in each state of a neural sequence. This control depends on the stability of each state, which is influenced by factors such as the external input strength, task difficulty, and top-down modulation. Our model coherently explains a variety of experimental findings and provides a unified theory for how the brain might flexibly adjust the speed of thought by taking advantage of diverse timescales of neural activity. © 2025 Elsevier Ltd | Metastable state; Multiple neural timescales; Sequential patterns; Speed modulation; Temporal scaling; Working memory | Brain; Cognitive systems; Dynamics; Neural networks; Neurons; Stability; Meta-stable state; Multiple neural timescale; Neural activity; Sequential patterns; Speed modulation; Task difficulty; Temporal modulations; Temporal scaling; Time-scales; Working memory; Computation theory; article; artificial neural network; cognition; controlled study; dwell time; human experiment; learning; mental performance; nerve cell network; nonhuman; speech; thinking; velocity; working memory | NaN | NaN | NaN | NaN | NaN | NaN | Amari, Shunichi, Learning patterns and pattern sequences by self-organizing nets of threshold elements, IEEE Transactions on Computers, C-21, 11, pp. 1197-1206, (1972); Beiran, Manuel, Contrasting the effects of adaptation and synaptic filtering on the timescales of dynamics in recurrent networks, PLOS Computational Biology, 15, 3, (2019); Benozzo, Danilo, Slower prefrontal metastable dynamics during deliberation predicts error trials in a distance discrimination task, Cell Reports, 35, 1, (2021); Bernacchia, Alberto, A reservoir of time constants for memory traces in cortical neurons, Nature Neuroscience, 14, 3, pp. 366-372, (2011); Boerlin, Martin, Predictive Coding of Dynamical Variables in Balanced Spiking Networks, PLOS Computational Biology, 9, 11, (2013); Bollimunta, Anil, Neural dynamics of choice: Single-trial analysis of decision-related activity in parietal cortex, Journal of Neuroscience, 32, 37, pp. 12684-12701, (2012); Cavanagh, Sean Edward, A Diversity of Intrinsic Timescales Underlie Neural Computations, Frontiers in Neural Circuits, 14, (2020); Cavanagh, Sean Edward, Reconciling persistent and dynamic hypotheses of working memory coding in prefrontal cortex, Nature Communications, 9, 1, (2018); Cavanagh, Sean Edward, Autocorrelation structure at rest predicts value correlates of single neurons during reward-guided choice, eLife, 5, OCTOBER2016, (2016); Chalk, Matthew, Neural oscillations as a signature of efficient coding in the presence of synaptic delays, eLife, 5, 2016JULY, (2016) | T. Kurikawa; Department of Complex and Intelligent Systems, Future University Hakodate, Hakodate Hokkaido, 116-2 Kamedanakano-cho, 041-8655, Japan; email: kurikawa@fun.ac.jp | NaN | Elsevier Ltd | NaN | NaN | NaN | NaN | NaN | 08936080 | NaN | NNETE | NaN | English | Neural Netw. | Article | Final | NaN | Scopus | 2-s2.0-105024706206 | 10.1016/j.neunet.2025.108381 |
| 7 | Jing, P.; Lee, K.; Zhang, Z.; Zhou, H.; Yuan, Z.; Gao, Z.; Zhu, L.; Papanastasiou, G.; Fang, Y.; Yang, G. | Jing, Peiyuan (59706029600); Lee, Kinhei (59145910100); Zhang, Zhenxuan (58928546300); Zhou, Huichi (58899029300); Yuan, Zhengqing (59057862700); Gao, Zhifan (55320405200); Zhu, Lei (56399719900); Papanastasiou, Giorgos (56539707000); Fang, Yingying (57204847825); Yang, Guang (57216243504) | 59706029600; 59145910100; 58928546300; 58899029300; 59057862700; 55320405200; 56399719900; 56539707000; 57204847825; 57216243504 | Reason like a radiologist: Chain-of-thought and reinforcement learning for verifiable report generation | 2026 | Medical Image Analysis | 109 | NaN | 103910 | NaN | NaN | 0 | 10.1016/j.media.2025.103910 | https://www.scopus.com/inward/record.uri?eid=2-s2.0-105024997351&doi=10.1016%2Fj.media.2025.103910&partnerID=40&md5=34be87236b93a12211a3c7bff4c3ffb0 | Imperial College London, London, United Kingdom; College of Engineering, Notre Dame, IN, United States; School of Biomedical Engineering, Sun Yat-Sen University, Guangzhou, Guangdong, China; The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China; Mathematics Research Centre, Academy of Athens, Athens, Attica, Greece; Archimedes Unit, Athena Research Centre, Athens, Greece; National Heart and Lung Institute, London, United Kingdom; Cardiovascular Research Centre, Royal Brompton Hospital, London, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom | Jing, Peiyuan, Imperial College London, London, United Kingdom; Lee, Kinhei, Imperial College London, London, United Kingdom; Zhang, Zhenxuan, Imperial College London, London, United Kingdom; Zhou, Huichi, Imperial College London, London, United Kingdom; Yuan, Zhengqing, College of Engineering, Notre Dame, IN, United States; Gao, Zhifan, School of Biomedical Engineering, Sun Yat-Sen University, Guangzhou, Guangdong, China; Zhu, Lei, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China; Papanastasiou, Giorgos, Mathematics Research Centre, Academy of Athens, Athens, Attica, Greece, Archimedes Unit, Athena Research Centre, Athens, Greece; Fang, Yingying, Imperial College London, London, United Kingdom, National Heart and Lung Institute, London, United Kingdom; Yang, Guang, Imperial College London, London, United Kingdom, National Heart and Lung Institute, London, United Kingdom, Cardiovascular Research Centre, Royal Brompton Hospital, London, United Kingdom, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom | Radiology report generation is critical for efficiency, but current models often lack the structured reasoning of experts and the ability to explicitly ground findings in anatomical evidence, which limits clinical trust and explainability. This paper introduces BoxMed-RL, a unified training framework to generate spatially verifiable and explainable chest X-ray reports. BoxMed-RL advances chest X-ray report generation through two integrated phases: (1) Pretraining Phase. BoxMed-RL learns radiologist-like reasoning through medical concept learning and enforces spatial grounding with reinforcement learning. (2) Downstream Adapter Phase. Pretrained weights are frozen while a lightweight adapter ensures fluency and clinical credibility. Experiments on two widely used public benchmarks (MIMIC-CXR and IU X-Ray) demonstrate that BoxMed-RL achieves an average 7 % improvement in both METEOR and ROUGE-L metrics compared to state-of-the-art methods. An average 5 % improvement in large language model-based metrics further underscores BoxMed-RL’s robustness in generating high-quality reports. Related code and training templates are publicly available at https://github.com/ayanglab/BoxMed-RL . © 2025 The Author(s). | Explainability; Radiology report generation; Reinforcement learning | Computational methods; Machine learning; Current modeling; Explainability; Learn+; Phase 1; Pre-training; Radiology report generation; Radiology reports; Reinforcement learnings; Report generation; Training framework; Radiology; article; benchmarking; human; large language model; radiologist; reasoning; thinking; thorax radiography; X ray; X ray analysis | NaN | NaN | NaN | NaN | NaN | NaN | Gpt 4 Technical Report, (2023); Bigolin Lanfredi, Ricardo, REFLACX, a dataset of reports and eye-tracking data for localization of abnormalities in chest x-rays, Scientific Data, 9, 1, (2022); Boecking, Benedikt, Making the Most of Text Semantics to Improve Biomedical Vision-Language Processing, Lecture Notes in Computer Science, 13696 LNCS, pp. 1-21, (2022); Brady, Adrian Paul, Discrepancy and error in radiology: Concepts, causes and consequences, Ulster Medical Journal, 81, 1, pp. 3-9, (2012); Chexpert Plus Augmenting A Large Chest X Ray Dataset with Text Radiology Reports Patient Demographics and Additional Image Formats, (2024); Chen, Zhihong, Cross-modal memory networks for radiology report generation, 1, pp. 5904-5914, (2021); Chen, Zhihong, Generating radiology reports via memory-driven transformer, pp. 1439-1449, (2020); Chen, Zhe, Intern VL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 24185-24198, (2024); Forty Second International Conference on Machine Learning, (2025); Comparative Interpretation of CT and Standard Radiography of the Chest, (2011) | G. Yang; Bioengineering Department and Imperial-X, Imperial College London, London, W12 7SL, United Kingdom; email: g.yang@imperial.ac.uk | NaN | Elsevier B.V. | NaN | NaN | NaN | NaN | NaN | 13618415 | NaN | MIAEC | NaN | English | Med. Image Anal. | Article | Final | All Open Access; Hybrid Gold Open Access | Scopus | 2-s2.0-105024997351 | 10.1016/j.media.2025.103910 |
| 8 | Ahmad, M.; Y.; Moreno-Benito, M.; S.; Rao, H.N.; Mustakis, J.; Karimi, I.A. | Ahmad, Maaz (57216171666); Moreno-Benito, Marta (36782636600); Rao, Harsha Nagesh (59544492700); Mustakis, Jason G. (26536355200); Karimi, Iftekar Abubakar (7005509050) | 57216171666; 60238154100; 36782636600; 60238125600; 59544492700; 26536355200; 7005509050 | Cluster-based adaptive sampling methodology for systems modeling | 2026 | Computers and Chemical Engineering | 206 | NaN | 109527 | NaN | NaN | 0 | 10.1016/j.compchemeng.2025.109527 | https://www.scopus.com/inward/record.uri?eid=2-s2.0-105024757523&doi=10.1016%2Fj.compchemeng.2025.109527&partnerID=40&md5=08f4a3dd38ef3c45e5b8118556ebcb9b | Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore City, Singapore; Worldwide Research and Development, Pfizer Inc., New York, NY, United States; Worldwide Research and Development, Pfizer Inc., Chennai, India; Pfizer Asia Manufacturing Pte Ltd., Singapore City, Singapore; Worldwide Research and Development, Pfizer Inc., New York, NY, United States | Ahmad, Maaz, Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore City, Singapore; null, null, Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore City, Singapore; Moreno-Benito, Marta, Worldwide Research and Development, Pfizer Inc., New York, NY, United States; null, null, Worldwide Research and Development, Pfizer Inc., Chennai, India; Rao, Harsha Nagesh, Pfizer Asia Manufacturing Pte Ltd., Singapore City, Singapore; Mustakis, Jason G., Worldwide Research and Development, Pfizer Inc., New York, NY, United States; Karimi, Iftekar Abubakar, Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore City, Singapore | Modeling real-world (experimental) or simulated (computational) systems using data-driven surrogate models involves selecting a sampling technique to generate the input-output data for training and selecting a surrogate form. In this work, we present a novel sampling technique, Cluster-based Adaptive Sampling, that generates training data smartly and adaptively for developing surrogate models over a given input domain. CAS iteratively clusters sampled points, defines Voronoi tessellation of cluster centroids, and approximates the tessellations using simple hypercubes. It then searches locally and globally over the domain at each iteration to identify nonlinear and under-explored regions respectively, where it samples two new points using a distance-based metric. CAS is agnostic to surrogate form and terminates automatically based on a surrogate quality metric. We assessed CAS against two existing sampling techniques on 40 diverse test functions using six surrogate forms. CAS outperformed both techniques in developing more accurate surrogates for a given computational effort and required lower computational effort for a specified accuracy across most test functions and forms. We highlight the practical applicability of CAS in modeling two pharmaceutical processes and showcase its superior performance over the two techniques. © 2025 | Active learning; Adaptive sampling; Design of experiments; Machine learning; Surrogate modeling; Systems modeling | Artificial intelligence; Fuel additives; Input output programs; Iterative methods; Learning systems; Systems analysis; Systems thinking; Test facilities; Active Learning; Adaptive sampling; Cluster-based; Computational effort; Machine-learning; Real-world; Sampling technique; Surrogate modeling; System models; Test-functions; Design of experiments | NaN | NaN | NaN | NaN | NaN | NaN | Ahmad, Maaz, Families of similar surrogate forms based on predictive accuracy and model complexity, Computers and Chemical Engineering, 163, (2022); Ahmad, Maaz, Surrogate Classification based on Accuracy and Complexity, Computer Aided Chemical Engineering, 49, pp. 1735-1740, (2022); Ahmad, Maaz, Revised learning based evolutionary assistive paradigm for surrogate selection (LEAPS2v2), Computers and Chemical Engineering, 152, (2021); Aute, Vikrant C., Cross-validation based single response adaptive design of experiments for Kriging metamodeling of deterministic computer simulations, Structural and Multidisciplinary Optimization, 48, 3, pp. 581-605, (2013); Bhakte, Abhijit, Alarm-based explanations of process monitoring results from deep neural networks, Computers and Chemical Engineering, 179, (2023); Bhosekar, Atharv, Advances in surrogate based modeling, feasibility analysis, and optimization: A review, Computers and Chemical Engineering, 108, pp. 250-267, (2018); Boukouvala, Fani, ARGONAUT: AlgoRithms for Global Optimization of coNstrAined grey-box compUTational problems, Optimization Letters, 11, 5, pp. 895-913, (2017); Boukouvala, Fani, Feasibility analysis of black-box processes using an adaptive sampling Kriging-based method, Computers and Chemical Engineering, 36, 1, pp. 358-368, (2012); Boukouvala, Fani, Global optimization advances in Mixed-Integer Nonlinear Programming, MINLP, and Constrained Derivative-Free Optimization, CDFO, European Journal of Operational Research, 252, 3, pp. 701-727, (2016); Boukouvala, Fani, Dynamic data-driven modeling of pharmaceutical processes, Industrial and Engineering Chemistry Research, 50, 11, pp. 6743-6754, (2011) | I.A. Karimi; Department of Chemical & Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore, 117585, Singapore; email: cheiak@nus.edu.sg | NaN | Elsevier Ltd | NaN | NaN | NaN | NaN | NaN | 00981354 | 008030270X; 9780080302706 | CCEND | NaN | English | Comput. Chem. Eng. | Article | Final | NaN | Scopus | 2-s2.0-105024757523 | 10.1016/j.compchemeng.2025.109527 |
| 9 | Jha, N.K.; Tsai, M.-J. | Jha, Nitesh Kumar (58658236100); Tsai, Meng Jung (7403551418) | 58658236100; 7403551418 | Using machine learning approaches to predict Taiwanese eighth graders' computational thinking performance in ICILS 2023 study | 2026 | Computers in Human Behavior Reports | 21 | NaN | 100896 | NaN | NaN | 0 | 10.1016/j.chbr.2025.100896 | https://www.scopus.com/inward/record.uri?eid=2-s2.0-105023962868&doi=10.1016%2Fj.chbr.2025.100896&partnerID=40&md5=575d8689eec33907c5983d1a39af339c | Program of Learning Sciences, National Taiwan Normal University, Taipei, Taiwan; Program of Learning Sciences, National Taiwan Normal University, Taipei, Taiwan | Jha, Nitesh Kumar, Program of Learning Sciences, National Taiwan Normal University, Taipei, Taiwan; Tsai, Meng Jung, Program of Learning Sciences, National Taiwan Normal University, Taipei, Taiwan | This study employs machine learning approaches to examine how socio-demographic, student-related, and school-related variables predict the computational thinking (CT) performance of 5211 Taiwanese eighth graders in the ICILS 2023 study (Fraillon, 2024). It further aims to identify the key predictors of Taiwanese students' CT scores in this international evaluation project. The study used seven trained models: Multinomial Logistic Regression, Random Forest, AdaBoost, XGBoost, LightGBM, Gradient Boosting classifier, and Stacking Ensemble to identify and rank the variables that affect CT scores. The CT performance score was used as a binary variable with two classes: below and above average score. Findings showed that XGBoost and Stacking Ensemble performed best when classifying below and average CT scores respectively in terms of precision, recall and F1 score. In addition, among the variables, student-related variables had the highest impact on students' CT skills followed by school-related and socio-demographic. Among student-related variables, CT disposition was the most significant variable followed by ICT self-efficacy and academic multitasking. Further, among school-related factor, learning special applications in class had significant impact followed by a low impact of socio-demographic variables such as home literacy and parents' education. This study offers practical implications for educators, policymakers, and curriculum designers by underscoring the role of CT disposition and recommending targeted support for enhancing students’ digital self-efficacy. Additionally, the study shows the potential of ML for creating adaptive learning environments and guiding data-informed decisions in educational policy and practice. © 2025 The Authors. | 21st century abilities; Applications in subject areas; Data science applications in education; Information literacy; Secondary education | NaN | NaN | NaN | NaN | NaN | National Taiwan Normal University, NTNU; International Association for the Evaluation of Educational Achievement, IEA; National Science and Technology Council, NSTC; Ministry of Education, MOE | The authors thank to the financial support from The National Science and Technology Council, Taiwan, and The Ministry of Education, Taiwan. Special thanks go to the colleagues from the IEA in Hamberg, Germany, the IEA in Amsterdam, Neitherlands, and the ICILS 2023 National Research Center at NTNU, Taiwan for their excellent research collaboration in the ICILS 2023 study. | Akiba, Takuya, Optuna: A Next-generation Hyperparameter Optimization Framework, pp. 2623-2631, (2019); Alhassan, Amal, Predict students' academic performance based on their assessment grades and online activity data, International Journal of Advanced Computer Science and Applications, 11, 4, (2020); Allen, Jeff, Third-year college retention and transfer: Effects of academic performance, motivation, and social connectedness, Research in Higher Education, 49, 7, pp. 647-664, (2008); Asselman, Amal, Enhancing the prediction of student performance based on the machine learning XGBoost algorithm, Interactive Learning Environments, 31, 6, pp. 3360-3379, (2023); Atmatzidou, Soumela, Advancing students' computational thinking skills through educational robotics: A study on age and gender relevant differences, Robotics and Autonomous Systems, 75, pp. 661-670, (2016); International Journal of Educational Methodology, (2020); Barr, Valerie B., Bringing computational thinking to K-12: What is involved and what is the role of the computer science education community?, ACM Inroads, 2, 1, pp. 48-54, (2011); Bradley, Janae, Increasing adoption rates at animal shelters: a two-phase approach to predict length of stay and optimal shelter allocation, BMC Veterinary Research, 17, 1, (2021); Bradley, Janae, Developing predictive models for early detection of intervertebral disc degeneration risk, Healthcare Analytics, 2, (2022); Annual American Educational Research Association Meeting, (2012) | M.-J. Tsai; Program of Learning Sciences, School of Learning Informatics, Institute for Research Excellence in Learning Sciences, National Taiwan Normal University, Taipei, 162, Sec. 1, Hoping E. Rd., 106, Taiwan; email: mjtsai99@ntnu.edu.tw | NaN | Elsevier B.V. | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | English | Comput. Hum. Behav. Rep. | Article | Final | All Open Access; Gold Open Access | Scopus | 2-s2.0-105023962868 | 10.1016/j.chbr.2025.100896 |
| Authors | Author full names | Author(s) ID | Title | Year | Source title | Volume | Issue | Art. No. | Page start | Page end | Cited by | DOI | Link | Affiliations | Authors with affiliations | Abstract | Author Keywords | Index Keywords | Molecular Sequence Numbers | Chemicals/CAS | Tradenames | Manufacturers | Funding Details | Funding Texts | References | Correspondence Address | Editors | Publisher | Sponsors | Conference name | Conference date | Conference location | Conference code | ISSN | ISBN | CODEN | PubMed ID | Language of Original Document | Abbreviated Source Title | Document Type | Publication Stage | Open Access | Source | EID | doi_norm | |
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| 12182 | Wheatley, G.H. | Wheatley, Grayson H. (36864672400) | 36864672400 | A Mathematics Curriculum for the Gifted and Talented | 1983 | Gifted Child Quarterly | 27 | 2 | NaN | 77 | 80 | 4 | 10.1177/001698628302700205 | https://www.scopus.com/inward/record.uri?eid=2-s2.0-67650426074&doi=10.1177%2F001698628302700205&partnerID=40&md5=2eb3af9cd5b078c190add8819d87d99b | Purdue University, West Lafayette, IN, United States | Wheatley, Grayson H., Purdue University, West Lafayette, IN, United States | Developing a mathematics program for the gifted is more than setting a faster pace through existing textbooks. It is important to step back and take a broad view of the problem. What type of thinking do we want to encourage? What do we want children to know? What modes of instruction are appropriate? This paper has outlined ten major strands in elementary school mathematics. The mathematics needs of the eighties and nineties will be different from the fifties and sixties. We must plan for the future. Certainly the gifted should be encouraged to reason and relate ideas. Problem solving is an excellent tool for this purpose. Computers are rapidly becoming a standard tool for thought and work. The gifted must learn to use them and this should include writing computer programs. With increased use of computers and calculators it is important that estimation skills be strong. Additionally, there is the necessity for acquiring concepts, principles, facts, and mathematical rules. We must strive to achieve the proper balance between computational skill and higher level thinking; both are important. A major thesis of this paper is that present textbooks do not develop higher level reasoning but over-emphasize computational rules. The ten strands described can form the basis for a balanced mathematics program for the gifted. © 1983, Sage Publications. All rights reserved. | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Bogen, Joseph E., The other side of the brain. II. An appositional mind., Bulletin of the Los Angeles neurological societies, 34, 3, pp. 135-162, (1969); Teaching Problem Solving What Why and how, (1982); Saber Tooth Curriculum, (2025); Guide to Using Estimation Skills and Strategies, (1983); Journal for Research in Mathematics Education, (1978); Calculator Use and Problem Solving Strategies of Grade Six Pupils Final Report, (1982) | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 00169862 | NaN | NaN | NaN | English | Gifted Child Q. | Article | Final | NaN | Scopus | 2-s2.0-67650426074 | 10.1177/001698628302700205 |
| 12183 | Osborn, H.H. | Osborn, Herbert H. (57017110100) | 57017110100 | The Assessment of Mathematical Abilities | 1983 | Educational Research | 25 | 1 | NaN | 28 | 40 | 2 | 10.1080/0013188830250104 | https://www.scopus.com/inward/record.uri?eid=2-s2.0-2842594331&doi=10.1080%2F0013188830250104&partnerID=40&md5=5a523c1b1e34666d7f2eec7f345fe485 | Goldsmiths, University of London, London, United Kingdom | Osborn, Herbert H., Goldsmiths, University of London, London, United Kingdom | Experience has suggested that the common,generalized assessment of mathematical ability by a single grade in a mathematics examination is inadequate.On the hypothesis that the thinking involved in mathematical activity may be resolved into four distinct,but not discreet, components: computational operations, pattern recognition, logical reasoning and the symbolic manipulation of abstract quantities (components C, P, L and 5), a test was devised and given to 322 pupils in fifth forms of seven secondary schools in the London and near-London area, a few months prior to their taking either the GCE O-level or CSE examinations in mathematics. From the scores gained in the test a profile was obtained for each pupil tested. A comparison of these profiles and of the vectors obtained from them with the grades gained in the examinations taken has enabled some important, albeit tentative, observations to be made on the structure of mathematics examinations and the processes of teaching the subject in schools. © 1983, Taylor & Francis Group, LLC. All rights reserved. | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Structure of Human Abilities, (1950); London University of London Press, (1960); Chicago University of Chicago Press, (1976); British Journal of Educational Psychology, (1977); Williams, John D., Teaching Emphases In Primary Mathematics, Educational Research, 14, 3, pp. 177-181, (1972); Wood, R., Objectives in the teaching of mathematics, Educational Research, 10, 2, pp. 83-98, (1968) | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 00131881 | NaN | NaN | NaN | English | Educ. Res. | Article | Final | NaN | Scopus | 2-s2.0-2842594331 | 10.1080/0013188830250104 |
| 12184 | Knapp, M.S. | Knapp, Martin Sayers (7202388642) | 7202388642 | Computing, mathematics, and the nephrologist. | 1983 | Kidney International | 24 | 4 | NaN | 433 | 435 | 1 | 10.1038/ki.1983.178 | https://www.scopus.com/inward/record.uri?eid=2-s2.0-0020837797&doi=10.1038%2Fki.1983.178&partnerID=40&md5=59431363cd7e6beeb648155a3b1ce585 | NaN | Knapp, Martin Sayers, | The potential of computing and mathematics to make major contributions to nephrology by making information retrieval and presentation more accurate, more complete, and more rapid and by providing immediate access to computational and graphic facilities is emphasized in this symposium issue. The realization that disordered physiology due to disease may not prevent the course of an illness being described in mathematical terms should encourage physicians, and others interested in pathophysiology, to integrate mathematics and statistics into their thinking and their practice. | NaN | article; biological model; computer; decision making; human; mathematics; medical record; nephrology; statistics; Computers; Decision Making; Humans; Mathematics; Medical Records; Models, Biological; Nephrology; Statistics; MLCS; MLOWN | NaN | NaN | NaN | NaN | Medical Research Council, MRC | The concepts discussed in this introduction evolved when the author was Consultant Renal Physician to the Nottingham Hospitals, and in receipt of grants from the Medical Research Council and the Notting- ham & Nottinghamshire Kidney Fund. | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 00852538 | NaN | NaN | 6645214.0 | English | Kidney Int. | Article | Final | NaN | Scopus | 2-s2.0-0020837797 | 10.1038/ki.1983.178 |
| 12185 | Wilson Pearson, L.W. | Wilson Pearson, L. (7103019064) | 7103019064 | Present thinking of the use of the singularity expansion in electromagnetic scattering computation | 1983 | Wave Motion | 5 | 4 | NaN | 355 | 368 | 7 | 10.1016/0165-2125(83)90022-7 | https://www.scopus.com/inward/record.uri?eid=2-s2.0-0020804776&doi=10.1016%2F0165-2125%2883%2990022-7&partnerID=40&md5=2cf0c0ae0f1f3b693aba4a54e2af6705 | Department of Electrical Engineering, University of Mississippi, University, MS, United States | Wilson Pearson, L., Department of Electrical Engineering, University of Mississippi, University, MS, United States | While the singularity expansion of electromagnetic scattering responses has received a great deal of attention over the last several years, a number of uncertainties have persisted in connection with its applicability and completeness. Recently, the dominant questions have been clarified, at least for one with pragmatic computational goals. This paper surveys the present understanding of the singularity expansion from the pragmatist's point-of-view. Attention is given to recent work which clarifies points which have been debated in the past. The interpretation of the expansion in the presence of a time-limited excitation function is discussed. Various means for determining the expansion parameters for a given object are surveyed. © 1983. | NaN | ELECTROMAGNETIC WAVES | NaN | NaN | NaN | NaN | Office of Naval Research, ONR, (N00014-81-K-0256) | This work was sponsored, in part, by the Office of Naval Research under Contract Number N00014-81-K-0256. | Interaction Notes, (1971); Electromagnetics, (1981); Transient Electromagnetic Fields, (1976); Kennaugh, Edward M., The K-Pulse Concept, IEEE Transactions on Antennas and Propagation, 29, 2, pp. 327-331, (1981); Wilson Pearson, L., The Extraction of the Singularity Expansion Description of a Scatterer from Sampled Transient Surface Current Response, IEEE Transactions on Antennas and Propagation, 28, 2, pp. 182-190, (1980); Cho, K. S., Calculation of the SEM Parameters from the Transient Response of a Thin Wire, IEEE Transactions on Antennas and Propagation, 28, 6, (1980); Wilson Pearson, L., SEM Parameter Extraction Through Transient Surface Current Measurement Using King-Type Probes, IEEE Transactions on Antennas and Propagation, 30, 2, pp. 260-266, (1982); Journal De L Ecole Polytechnique De Paris, (1795); Applied Analysis, (1956); Marin, Lennart, Natural-Mode Representation of Transient Scattered Fields, IEEE Transactions on Antennas and Propagation, 21, 6, pp. 809-818, (1973) | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 01652125 | NaN | NaN | NaN | English | Wave Mot. | Article | Final | NaN | Scopus | 2-s2.0-0020804776 | 10.1016/0165-2125(83)90022-7 |
| 12186 | Forsyth, R.A.; Ansley, T.N. | Forsyth, Robert A. (57029929200); Ansley, Timothy N. (6505980402) | 57029929200; 6505980402 | The importance of computational skill for answering items in a mathematics problem solving test: Implications for construct validity | 1982 | Educational and Psychological Measurement | 42 | 1 | NaN | 257 | 263 | 1 | 10.1177/0013164482421032 | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84973850769&doi=10.1177%2F0013164482421032&partnerID=40&md5=b13e03d4b57537a03d6020ecd92b0791 | University of Iowa, Iowa City, IA, United States | Forsyth, Robert A., University of Iowa, Iowa City, IA, United States; Ansley, Timothy N., University of Iowa, Iowa City, IA, United States | The primary purpose of this study was to investigate the importance of computational skill for answering items in the Quantitative Thinking subtest (Test Q) of the Iowa Tests of Educational Development (ITED). Nine matched pairs of schools participated in the study. One school from each pair allowed students to use calculators when taking Test Q, while the other school did not allow calculators to be employed. The difficulty levels of the items in Test Q were calculated for both test conditions. In general, the differences in p values were very small. On the basis of these results, it was concluded that computational skill is not a major factor contributing to an examinee's score on Test Q and thus that the use of Test Q as a measure of problem solving ability is not compromised by its computational requirements. © 1982, Sage Publications. All rights reserved. | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Iowa Tests of Educational Development Form X 8, (1993); Mathematics Teacher, (1978); Forsyth, Robert A., MEASURING PROBLEM SOLVING ABILITY IN MATHEMATICS WITH MULTIPLE‐CHOICE ITEMS: THE EFFECT OF ITEM FORMAT ON SELECTED ITEM AND TEST CHARACTERISTICS, Journal of Educational Measurement, 17, 1, pp. 31-43, (1980); Iowa Tests of Basic Skills, (1964); Arithmetic Teacher, (1977); Today S Education, (1977); Arithmetic Teacher, (1977) | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 00131644 | NaN | NaN | NaN | English | Educ. Psychol. Meas. | Article | Final | NaN | Scopus | 2-s2.0-84973850769 | 10.1177/0013164482421032 |
| 12187 | Cohen, M.D. | Cohen, Michael D. (57194515983) | 57194515983 | The power of parallel thinking | 1981 | Journal of Economic Behavior and Organization | 2 | 4 | NaN | 285 | 306 | 35 | 10.1016/0167-2681(81)90011-1 | https://www.scopus.com/inward/record.uri?eid=2-s2.0-0000099394&doi=10.1016%2F0167-2681%2881%2990011-1&partnerID=40&md5=34e606f95840401d7b7cefc0db1706bb | University of Michigan, Ann Arbor, Ann Arbor, MI, United States | Cohen, Michael D., University of Michigan, Ann Arbor, Ann Arbor, MI, United States | A small computer model demonstrates that an appropriate organization of boundedly rational individuals can find optimal policies in an environment that is overwhelmingly complex for unorganized decision makers. The model is also used to identify conditions under which optimal - or even good - policies are not found. The demonstrated adaptive power of the model is interpreted in light of recent developments in the theory of computational complexity that place new stress on powerful methods of search, and of new models from computer science which markedly advance search effectiveness by harnessing parallel structures of information processing. © 1981. | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Organizations and Environments, (1979); Cognitive Psychology and Its Implications, (1990); Borosh, Itshak, Bounds on positive integral solutions of linear diophantine equations, Proceedings of the American Mathematical Society, 55, 2, pp. 299-304, (1976); Discussion Paper no 151, (1980); Discussion Paper no 153, (1982); Cook, Stephen A., The complexity of theorem-proving procedures, Proceedings of the Annual ACM Symposium on Theory of Computing, pp. 151-158, (1971); Bell Journal of Economics, (1980); Handbook of Learning and Cognitive Processes, (1975); Netl A System for Representing and Using Real World Knowledge, (1979); Computers and Intractability, (1979) | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 01672681 | NaN | JEBOD | NaN | English | J. Econ. Behav. Organ. | Article | Final | NaN | Scopus | 2-s2.0-0000099394 | 10.1016/0167-2681(81)90011-1 |
| 12188 | Fearnley-Sander, D. | Fearnley-Sander, Desmond (6506443658) | 6506443658 | Learning to calculate and learning mathematics | 1980 | International Journal of Mathematical Education in Science and Technology | 11 | 1 | NaN | 111 | 114 | 0 | 10.1080/0020739800110117 | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84946291105&doi=10.1080%2F0020739800110117&partnerID=40&md5=b78435cdc181811acd652e2e1f95919d | Department of Mathematics, University of Tasmania, Hobart, TS, Australia | Fearnley-Sander, Desmond, Department of Mathematics, University of Tasmania, Hobart, TS, Australia | A calculator solution of a simple computational problem is discussed with emphasis on its ramifications for the understanding of some fundamental theorems of pure mathematics and techniques of computing. Today's mathematics teachers grew up under the influence of the formalist, structuralist tendencies which dominated thinking about mathematics between the nineteen thirties and the sixties. In the last decade or two, though, a new influence, constructive and algorithmic, has become increasingly important and it is now about to have an effect upon mathematical education in the schools. This is very healthy; for, whatever may be their relative importance in mathematics as a whole, it is clear that the algorithmic approach has much to say to the child which formalism leaves out. In a word, it shifts the emphasis from abstractions about sets and relations to concrete facts about something of direct and immediate importance to him: numbers. © Taylor & Francis Group, LLC. | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0020739X | NaN | NaN | NaN | English | Int. J. Math. Educ. Sci. Technol. | Article | Final | NaN | Scopus | 2-s2.0-84946291105 | 10.1080/0020739800110117 |
| 12189 | Basco, D.R. | Basco, David R. (7006364457) | 7006364457 | COMPUTATIONAL METHODS TO MODEL UNSTEADY VARIABLE DENSITY FLOWS IN HYDRAULIC DREDGING. | 1977 | NaN | v | 1 | NaN | J4 | J4 | 0 | NaN | https://www.scopus.com/inward/record.uri?eid=2-s2.0-0017432824&partnerID=40&md5=215bbcee4fca7f4827c77971b0ec1c67 | NaN | Basco, David R., | All dredging processes involve unsteady flows. We can now model the complicated variations of slurry density, velocity and pressure with time in the piping system by the use of high speed computers and finite-difference techniques. This paper outlines how these computations can be accomplished and qualitatively discusses some ramifications on present thinking based on steady-state analysis. | NaN | MATHEMATICAL TECHNIQUES - Finite Difference Method; Dredging; consen1tion; dispersion; equation of state; expansion; finite differenc; hydraulic transport; mass; method; momentum; pipeline; pressure; transport; turbulent | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | BHRA (Br Hydromech Res Assoc) Fluid Eng | NaN | Pap presented at the Int Symp on Dredging Technol, 2nd | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Article | Final | NaN | Scopus | 2-s2.0-0017432824 | NaN |
| 12190 | Flaherty, E.G. | Flaherty, E. G. (57011033900) | 57011033900 | The thinking aloud technique and problem solving ability | 1975 | Journal of Educational Research | 68 | 6 | NaN | 223 | 225 | 18 | 10.1080/00220671.1975.10884753 | https://www.scopus.com/inward/record.uri?eid=2-s2.0-0010997242&doi=10.1080%2F00220671.1975.10884753&partnerID=40&md5=53ffb760abe254c9977031742360c604 | Nasson College, Springvale, ME, United States | Flaherty, E. G., Nasson College, Springvale, ME, United States | The effects of overt verbalization and practice on problem solving ability were examined. The 100 secondary school students who served as Ss were divided into four groups: (1) those who received practice word problems and solved problems while thinking aloud, (2) those who did not practice but solved problems while thinking aloud, (3) those who practiced but dkl not verbalize, and (4) those who received no practice and did not verbalize. Analysis of variance revealed that neither overt verbalization nor practice significantly influenced problem solving scores. However, Ss who were required to think aloud made significantly more computational errors than those who worked without verbalizing. © Taylor & Francis. | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Broverman, Donald M., Individual differences in task performance under conditions of cognition interference, Journal of Personality, 26, 1, pp. 94-105, (1958); Brunk, Larry, A correlational study of two reasoning problems, Journal of Experimental Psychology, 55, 3, pp. 236-241, (1958); Problem Solving, (1966); Cognitive Processes Used in Solving Mathematical Problems Unpublished Doctoral Dissertation, (1973); Psychological Monographs, (1957); Psychological Reports, (1957); Principles of Psychology, (1890); Kilpatrick, Jeremy, 10: Problem Solving in Mathematics, Review of Educational Research, 39, 4, pp. 523-534, (1969); Problem Solving, (1966); Patrick, Catharine, Creative thought in artists, Journal of Psychology, 4, 1, pp. 35-73, (1937) | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 00220671 | NaN | NaN | NaN | English | J. Educ. Res. | Article | Final | NaN | Scopus | 2-s2.0-0010997242 | 10.1080/00220671.1975.10884753 |
| 12191 | Wegner, P. | Wegner, Peter (7005517088) | 7005517088 | Three Computer Cultures: Computer Technology, Computer Mathematics, and Computer Science | 1970 | Advances in Computers | 10 | C | NaN | 7 | 78 | 40 | 10.1016/S0065-2458(08)60431-3 | https://www.scopus.com/inward/record.uri?eid=2-s2.0-41249083505&doi=10.1016%2FS0065-2458%2808%2960431-3&partnerID=40&md5=ab66e09c65ad8c54442de0c0f04ba621 | Department of Computer Science, Providence, RI, United States | Wegner, Peter, Department of Computer Science, Providence, RI, United States | As scientific and technological tools, computers have proved so useful that computer science is widely regarded as a technological discipline whose purpose is to create problem-solving tools for other disciplines. Within computer science there is a group of theoreticians who build mathematical models of computational processes. Yet computer science is neither a branch of technology nor a branch of mathematics. It involves a new way of thinking about computational schemes that is partly technological and partly mathematical but contains a unique ingredient that differs qualitatively from those of traditional disciplines. This chapter illustrates the special quality that distinguishes computer science from technology and mathematics by the means of examples from the emerging theory of programming languages. The computer revolution is comparable to the industrial revolution. Just as machines have reduced the physical drudgery of man, computers are reducing his mental drudgery. The central role played by ‘energy’ in the industrial revolution is replaced in the computer revolution by ‘information.’ This chapter focuses on technological and scientific programming languages and mathematical models related to computers. © 1970, Academic Press Inc. | NaN | NaN | NaN | NaN | NaN | NaN | National Science Foundation, NSF, (GP7347); National Science Foundation, NSF | approach to computer science, while the characterization of languages and systems constitutes a “top-down” approach. The prosent paper is concerned principally with “top-down” computer science. A descriptive model for broad classes of computation is developed, and a number of questions associated with the modeling of programming languages are discussed in some detail to show that there are problems in computer science that are neither mathematical nor technological. This work was supported in part by NSF grant GP7347. | Theories of Abstract Automata, (1969); Information Theory, (1965); Computer Journal, (1963); Computer J, (1969); Handbook of Mathematical Psychology, (1963); Proc Extensible Languages Symp SIGPLAN Notices, (1969); Annals of Mathematics Studies, (1941); External Specifications for A Common Business Oriented Language, (1962); Universal Algebra, (1965); Combinatory Logic, (1958) | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 00652458 | 0123737478; 0120121662; 9780128138526; 9780323910897; 0120121670; 012373746X; 9780128171578; 9780123737465; 9780323898102; 9780128137864 | NaN | NaN | English | Adv. Comput. | Article | Final | NaN | Scopus | 2-s2.0-41249083505 | 10.1016/s0065-2458(08)60431-3 |